This upload consists of Ph.D dissertation defense slides of the research topic "Efficient VLSI Architectures for Image Enhancement Techniques" of Visvesvaraya Technological University, Belgaum, Karnataka, India.
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Ph.D Dissertation Defense Slides on Efficient VLSI Architectures for Image Enhancement Techniques
1. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Efficient VLSI Architectures for Image
Enhancement Techniques
Ph. D Dissertation Defense
M. C. Hanumantharaju - 1DS07MEN02
Research Scholar
Dr. M. Ravishankar
Research Advisor
Department of Information Science and Engineering
Dayananda Sagar College of Engineering, Bangalore-560078
March 7, 2014
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 1/146
2. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Efficient VLSI Architectures for Image
Enhancement Techniques
Ph. D Dissertation Defense
M. C. Hanumantharaju - 1DS07MEN02
Research Scholar
Dr. M. Ravishankar
Research Advisor
Department of Information Science and Engineering
Dayananda Sagar College of Engineering, Bangalore-560078
March 7, 2014
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 2/146
3. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Outline
1 Introduction
2 Motivation & Objectives
3 Contributions
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm &
Architecture
4 Conclusions & Future Scope
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 3/146
4. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Image Enhancement : An Introduction
Image processing
2-D signal processing
Improves characteristics, properties and parameters
Image enhancement
Key step in image processing
Modifies the attributes of an image
Makes it appropriate for analysis, diagnosis, and display.
Some of the image enhancement applications include
Sharpening: improves car license plate number
Contrast enhancement: medical image enhancement.
Edge enhancement: enhances objects in aerial image.
The realm of image enhancement wraps up
Reconstruction & Restoration
Filtering
Segmentation
Compression & Transmission
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 4/146
5. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Image enhancement : An Example
(a) Original Image (b) Enhanced Image
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 5/146
6. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Challenges
Image enhancement algorithms have numerous parame-
ters to specify and that needs to be adjusted to obtain
satisfactory results.
Lack of integrated algorithms.
Presently, image enhancement research demands better
reconstruction of high quality images than possible with
available researcher methods.
Image enhancement algorithms depends on the input im-
ages instead of adapting to its local features.
Limited speed achieved in software implementation since
image enhancement algorithms consists of large array of
data.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 6/146
7. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Choice of Implementations
General Purpose Processors
(GPPs)
Flexible.
Technology limits the pro-
cessing speed.
Limited performance.
Instruction sets are not
suitable for fast processing
of high resolution images.
GPP instructions are se-
quential and hence system
throughput decreases.
Digital Signal Processors
(DSPs)
Improvement over GPPs.
Falls between GPPs and
ASICs.
Inadequate pipelining and
parallel processing.
Fixed architectures that
limits the performance.
Parallel operation is possi-
ble with multiple DSPs.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 7/146
8. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Choice of Implementation Cont.,
Application Specific ICs
(ASICs)
Fast & efficient.
Fixed circuit.
Large time to market.
High cost, except for large
volume commercial appli-
cations.
No optimization.
Field Programmable Gate
Arrays (FPGAs)
High throughput.
Dynamically reconfig-
urable.
Massive pipelining and
parallelism.
Cost effective.
Attractive choice for real-
ization of DIP algorithms.
Present Research Work uses FPGAs for
Implementation
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 8/146
9. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Motivation
The motivation behind this work is to bring out the fea-
tures in the image that are not clearly visible owing to
different illumination conditions.
Current market demands better reconstruction of high
quality images than is possible with currently available
research outputs.
Limitations of image enhancement schemes: difficult to
tune parameters, deficit of integrated algorithms, lack of
quantitative standard, dependence on inputs instead of
adapting to local features.
Software implementation : inadequate speed.
Hardware implementation of image enhancement algo-
rithm is in great demand for applications such as medical,
forensic and surveillance etc.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 9/146
10. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Objectives
Development of efficient VLSI architectures for image en-
hancement algorithms.
Design & simulate the algorithm using software approach
(C or Matlab).
Test the algorithm for images having different environ-
mental conditions.
Realize the algorithm using HDL (Verilog or VHDL).
Verify both software & hardware implementation results.
Evaluate the efficiency of the algorithm using performance
metrics such as PSNR, contrast, luminance, IEF and wavelet
energy etc.
Compare proposed approach with other existing methods.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 10/146
12. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter (AROF)
Non-linear filter.
AROF is a powerful technique for denoising an image cor-
rupted by salt & pepper noise or impulse noise.
Impulse noise is often introduced into digital images dur-
ing image acquisition or Interference during transmission.
AROF not only adapts filter output but also window size
: iterative algorithm.
AROF window expands : All Pixels within the current
window are noisy or median itself is noisy.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 12/146
13. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter : Flow Chart
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 13/146
14. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Noisy Lena (90% Noise Level) and Restored Image
Figure: First Image : Lena Image with High Noise Density (90% Salt &
Pepper Noise) Second Image : Restored Lena Image using AROF.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 14/146
15. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work
Andreadis et al.1 proposed FPGA implementation of real-
time adaptive image impulse noise suppression. However,
the system slows down for highly corrupted images.
An efficient hardware implementation of weighted median
filter using cumulative histogram proposed by Fahmy et
al.2 reduces impulse noise satisfactorily. However, hard-
ware complexity is high for smaller window size.
1
I. Andreadis, G. Louverdis, ”Real-time Adaptive Image Impulse Noise Sup-
pression”, IEEE Tran. on Instrumentation and Measurement, Vol. 53, Issue 3,
pp. 798-806, 2004.
2
S. A Fahmy, P.Y.K. Cheung and W. Luk, ”Novel FPGA-based implementa-
tion of median and weighted median filters for image processing”, International
Conference on Field Programmable Logic and Applications, pp. 142-147, 2005.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 15/146
16. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont..,
Meena et al.3 proposed a optimized architectures for rank
Order filter. However, optimizations are done for sorting
network with out considering noise levels in an image.
Chih et al.4 proposed an efficient denoising architecture
for impulse noise removal in images. Although, decision
tree based approach used in this scheme is effective for
hardware implementation, technique may not provide sat-
isfactory results for images corrupted with high noise den-
sity.
3
S. M Meena and K. Linganagouda, ”Implementation and Analysis of Opti-
mized Architectures for Rank Order Filter”, Journal of Real Time Image Pro-
cessing, Vol. 3, Issue 1-3, pp. 33-41, 2008.
4
Chih-Yuan Lien, Chien-Chuan Huang, Pei-Yin Chen and Yi-Fan Lin in IEEE
Tran. on Computers, Vol. 62, No. 4, pp. 631-643, April 2013.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 16/146
17. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Advantages of Proposed Method
The proposed AROF reduces impulse noise without
degrading image information.
The reconstructed images using AROF provides better
visual quality than possible with Adaptive Median Filter
(AMF).
AROF consists of sliding window, sorting network,
median selection etc. Therefore, the proposed AROF is
best suited for FPGA implementation.
AROF has better performance than the AMF when the
noise density is moderate or high.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 17/146
18. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter : Illustration
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 18/146
19. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter : Illustration Cont..,
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 19/146
20. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter : Illustration Cont..,
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 20/146
21. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Rank Order Filter : Illustration Cont..,
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 21/146
22. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Top Level Module of AROF
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 22/146
23. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of AROF
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 23/146
24. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
3 × 3 Sliding Window Example
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 24/146
25. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of 3 × 3 Sliding Window
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 25/146
26. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of 5 × 5 Sliding Window
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 26/146
27. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of 7 × 7 Sliding Window
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 27/146
28. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Noise Detection Unit and Sorting Network Modules
(a) Impulse Noise Detector (b) Nine Element Sorting Module
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 28/146
29. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Details of Nine Element Sorter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 29/146
30. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Details of Nine Element Sorter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 30/146
31. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture of Compare & Swap Module
(c) Top Architecture of Compare &
Swap
(d) Detailed Architecture of Com-
pare & Swap
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 31/146
32. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Sorting Networks : Comparison for 9 Elements
Bubble sort : 36 comparators, 14 parallel Operations.
Bose Nelson sort : 27 comparators, 11 parallel
operations.
Hibbard sort : 27 comparators, 12 parallel operations.
Bitonic sort5 : 28 comparators, 8 parallel operations.
Batchers merge exchange sort6 : 27 comparators, 8
parallel operations.
Optimal sort : 25 comparators, 8 parallel operations
(proposed).
5
Zdenek Vasicek and Lukas Sekanina, ”Novel Hardware Implementation of
Adaptive Median Filters”, in proceedings of 11th IEEE Workshop on Design and
Diagnostics of Electronic Circuits and Systems, pp. 1-6, April, 2008.
6
Baddar, Sherenaz W. Al-Haj, and Kenneth E. Batcher, ”The AKS Sorting
Network,” Designing Sorting Networks, Springer New York, pp. 73-80, 2011.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 32/146
33. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab Simulation Results of AMF and AROF for
”Lena” Image
Figure: First Row: Original Images with Noise Level 20%, 40%, & 60%,
Second Row: AMF Reconstructed Images, Third Row: AROF
Recontructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 33/146
34. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab Simulation Results of AMF and AROF for
”House” Image
Figure: First Row: Original Images with Noise Level 20%, 40%, & 60%,
Second Row: AMF Reconstructed Images, Third Row: AROF
Recontructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 34/146
35. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
PSNR & IEF Computation
The Peak Signal to Noise Ration (PSNR) & Image Enhancement
Factor (IEF) are computed as follows:
PSNR = 10 log10
2552
MSE
MSE = 1
M×N
M
i=1
N
j=1
ˆI(i, j) − I(i, j)
2
where E(x, y) is the enhanced gray element at position (x, y), I(x,
y) is the original gray element at position (x, y) and, p and q denote
the size of the gray image.
IEF =
M
i=1
N
j=1[n(i,j)−I(i,j)]2
M
i=1
N
j=1[f (i,j)−I(i,j)]2
where n(x, y) is the noisy image, I(x, y) is the original image and
f(x, y) is the reconstructed image.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 35/146
36. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Quality Assessment using PSNR (dB) and IEF for
Lena and House Image
Figure: First Row: PSNR for Lena & House Image, Second Row: IEF
for Lena & House Image
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 36/146
37. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
ModelSim Simulation Results : Validity of Input
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 37/146
38. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
ModelSim Simulation Results : AROF Pixel Output
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 38/146
39. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Diagram for Illustrating the Pipelining
Operation of AROF System
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 39/146
40. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RTL View of the Top Module AROF System
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 40/146
41. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Zoomed View of the Top Module
Figure: U1: Impulse Noise Detection, U2: Sliding Window, U3: Sorting
Network, U4: Median Computation, U5: Delay Unit and U6: Output
Selection
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 41/146
42. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
FPGA (XC5VLX50-1FF1153) Device Utilization
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 42/146
43. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Summary
The timing summary for the AROF system as reported by Xilinx ISE
tool is as follows:
Speed Grade : -1
Minimum period: 4.392ns (Maximum Frequency:
227.668 MHz)
Minimum input arrival time before clock: 1.154ns
Maximum output required time after clock: 4.101ns
Clock period: 4.392ns (frequency: 227.668MHz)
Total number of paths / destination ports: 50736 / 5200
Delay: 4.392ns (Levels of Logic = 5)
Total number of paths / destination ports: 8 / 8
Offset: 1.154ns (Levels of Logic = 1)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 43/146
44. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab & Hardware Results of ”Butterfly” Image
Figure: First Row: Original Images with Noise Levels 20%, 40%, &
60%, Second Row: Matlab Reconstructed Images, Third Row:
Hardware Recontructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 44/146
45. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab & Hardware Results of ”Traffic Signal”
Image
Figure: First Row: Original Images with Noise Levels 20%, 40%, &
60%, Second Row: Matlab Reconstructed Images, Third Row:
Hardware Recontructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 45/146
46. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Quality Assessment using PSNR (dB) and IEF for
Lena and House Image
Figure: First Row: PSNR for ”Butterfly” & ”Traffic Signal” Image,
Second Row: IEF for ”Butterfly” & ”Traffic Signal” Image
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 46/146
47. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Summary
Table: Comparison of Present Implementation of AROF with
Another Implementation
Parameter Present Implementation Benkrid7
Picture Size (Pixels) 1600 × 1200 512 × 512
Frame Rate 118 25
7
K. Benkrid, D. Crookes, and A. Benkrid, ”Design and Implementation of
a Novel Algorithm for General Purpose Median Filtering on FPGA’s”, In IEEE
International Symposium on Circuits and Systems, (ISCAS2002), Vol. 4, pp.
425-428, 2002.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 47/146
48. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Publication Details
An Efficient VLSI Architecture for Adaptive Rank Order
Filter for Image Noise Removal, in the International Con-
ference on Signal Acquisition & Processing (ICSAP2011),
Singapore, 26-28 Feb, 2011.
A Novel FPGA Implementation of Adaptive Rank Order
Filter for Image Noise Removal, In an International Jour-
nal of Computer and Electrical Engineering (IJCE), Vol.
4, No. 3, June 2012.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 48/146
49. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Adaptive Color Image Enhancement using GMF
HSV color space is adapted since it separates color from
intensity.
The contrast of color image is enhanced by providing high
frequency spatial information from the saturation compo-
nent into luminance component.
Local correlation is computed for luminance enhancement
using Geometric Mean Filter (GMF).
Saturation component is enhanced by stretching its dy-
namic range.
The hue component of HSV is preserved in order to avoid
color distortion or shifting.
Sobel operator is used in order to smooth edges.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 49/146
50. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work
Jayanta Mukherjee et al.8 proposed enhancement of color
images by scaling DCT coefficients.
Advantages
Chromatic components are processed along with
luminance.
Visual quality is improved by reducing halo artifacts.
Disadvantages
This scheme violate gray world assumption.
Improves the quality of an image at the cost of
computational complexity.
8
Jayanta Mukherjee and Sanjit K. Mitra, ”Enhancement of Color Images by
Scaling the DCT Coefficients,” IEEE Transactions on Image Processing, Vol. 17,
No. 10, pp. 1783-1794, 2008.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 50/146
51. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Gang Song et al.9 proposed adaptive color image
enhancement based on human visual properties in HSV
space.
Advantages
Image enhancement is based on arithmetic mean &
variance computation.
Smooths local variations in an image.
Noise is reduced to some extent.
Disadvantages
Results in blurring effect.
Image details such as sharpness and edges are not
satisfactory.
9
Gang Song and Xiang-Lei Qiao, ”Adaptive Color Image Enhancement based
on Human Visual Properties,” in 3rd IEEE Conference on Industrial Electronics
and Applications (ICIEA 2008), pp. 1892-1895, 2008.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 51/146
52. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Tsai et al.10 proposed fast dynamic range compression
with local contrast preservation algorithm.
Advantages
The contrast enhancement operation in this scheme is
achieved by adaptive intensity transfer function and linear
color remapping techniques.
Processes 30 frames per second with the resolution of
640 × 480 pixels.
Disadvantages
However, there is still considerable room for optimization
of the design based on Verilog coding since the author
uses C++ for the design entry.
10
Chi-Yi Tsai, ”A Fast Dynamic Range Compression with Local Contrast
Preservation Algorithm and its Application to Real-Time Video Enhancement,”
IEEE Transactions on Multimedia, Vol. 14, Issue 4, pp. 1140-1152, 2012.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 52/146
53. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Ming et al.11 has developed high performance
architecture for enhancement of the video stream
captured under non-uniform lightning conditions.
Advantages
The RGB to HSV color space conversion adapted in this
scheme use log-domain in order to avoid complex division
process.
Processes 30 frames per second with the resolution of
640 × 480 pixels.
Disadvantages
Color space conversion and image enhancement operation
increases the latency of the system.
The HSV to RGB converter modules developed to achieve
reconstructed RGB images is not efficient from
computation point of view.
11
M. Z Zhang, M. J Seow, and V. K Asari, ”A High Performance Architecture
for Color Image Enhancement using a Machine Learning Approach,” in Interna-
tional Journal of Computational Intelligence, Vol. 2, Issue 1, pp. 40-47, 2006.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 53/146
54. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Stefano Marsi et al.12 proposed illumination reflectance
video enhancement based on FPGA implementation. Al-
though, this scheme reduces halo artifacts, large sized fil-
ter module, multiplier module and divider block exploited
in this work increases the computation complexity.
Faming et al.13 proposed a new pixel based variational
model for remote sensing multi-source image fusion using
gradient features. Visual inspection of the reconstructed
image reveals distortion in the spectral information while
merging the multi-spectral data.
12
Stefano Marsi and G. Ramponi, ”A Fexible FPGA Implementation for
Illuminance-refectance video enhancement,” Journal of Real-Time Image Pro-
cessing, pp. 1-13, 2011.
13
Guixu Zhanga Faming Fang, Fang Li and Chaomin Shen, ”A Variational
Method for Multi-source Remote-Sensing Image Fusion,” in International Journal
of Remote Sensing, Vol. 34, Issue 7, pp. 2470-2486, 2013.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 54/146
55. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Algorithm Steps
Step 1 : Read the color image.
Step 2 : Transform the image from RGB to HSV color
space.
Step 3 : Separate composite HSV into individual hue
(H), saturation (S), and value (V) components.
Step 4 : Enhance value and saturation components
adaptively based on geometric mean filter.
Step 5 : Preserve hue in order to avoid color distortion.
Step 6 : Combine separated components of H, S and V
into composite HSV.
Step 7 : Transform back HSV to RGB color space.
Step 8 : Display the enhanced color image.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 55/146
56. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Enhancement Equations
Local Geometric Mean for Value or Luminance is given by :
Vw = 1
mn [ i,j∈w V (i, j)]
1
mn
where m, n represents window
size, V is the luminance,Vw is the geometric mean for
luminance.
Local Geometric Mean for Saturation is given by :
Sw = 1
mn [ i,j∈w S(i, j)]
1
mn
where m, n represents window
size, S is the saturation,Sw is the geometric mean for
saturation.
Local Variance for Value or Luminance is given by :
σ2
v (x, y) = i,j∈w V (i, j) − Vw (x, y)
2
where x, y is the
center pixel within the window.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 56/146
57. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Enhancement Equations Cont.,
Local Variance for Saturation is given by :
σ2
s (x, y) = i,j∈w S(i, j) − Sw
2
Local Correlation Coefficient for luminance and saturation is
given by :
ρ(x, y) = i,j∈w [V (i,j)−Vw ][S(i,j)−Sw ]√
σ2
v (x,y)σ2
s (x,y) where ρ(x, y) is an
adaptive measure for luminance enhancement.
T1(x, y) = K1 V (x, y) − Vw (x, y)
T2(x, y) = K2 S(x, y) − Sw (x, y) ρ(x, y) where K1 and K2
are constants which is assumed as 2.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 57/146
58. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Enhancement Equations Cont.,
New Luminance Enhancement with Saturation Feedback is
given by :
Venh(x, y) = V (x, y) + T1(x, y) − T2(x, y)
where Venh(x, y) is the enhanced luminance.
The saturation enhancement is given by :
Senh(x, y) = [S(x, y)]γ
where S(x, y) is the saturation component, γ is the stretch
coefficient which determines the degree of saturation
enhancement, Senh(x, y) is the enhanced saturation
component.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 58/146
59. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Advantages of Proposed Method
The proposed method has low artifacts or noise.
Better contrast & Improved sharpness.
Our method avoids color distortion since hue is preserved.
Enhanced images are richer in color, have more clarity and
better visual effects.
Provides a general framework for ”Cross-Component Un-
Sharp Masking (USM)”, which is a color enhancement
strategy that may be applied to components from any
color space.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 59/146
60. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Block Diagram of the Proposed AGMF
Figure: Block Diagram of the Proposed Adaptive Color Image
Enhancement Method based on Geometric Mean Filter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 60/146
61. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Flow Chart for RGB to HSV Color Space Conversion
Figure: Flow Chart for RGB to HSV Color Space Conversion
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 61/146
62. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Flow Chart for HSV to RGB Color Space Conversion
Figure: Flow Chart for HSV to RGB Color Space Conversion
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 62/146
63. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Top Architecture of RGB-HSV Color Space
Conversion
(a) Signal diagram for HSV to RGB
Color Space Converter
(b) Signal diagram for RGB to HSV
Color Space Converter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 63/146
64. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture for RGB to HSV Color Space Converter
Figure: Detailed Architecture for RGB to HSV Color Space
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 64/146
65. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture for HSV to RGB Color Space Converter
Figure: Detailed Architecture for HSV to RGB Color Space
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 65/146
66. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture Details of AGMF
Figure: Detailed Signal Diagram of Adaptive Color Image
Enhancement based on Geometric Mean Filter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 66/146
67. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture for Geometric Mean Filter Module
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 67/146
68. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture for Geometric Mean Filter Module
Cont.,
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 68/146
69. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architecture for Histogram Equalization
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 69/146
70. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab Simulation Results of ”Tractor” Image
Figure: (a) Original Image (b) Histogram Equalized Image (c) Image
Enhanced using Gang et al. Method & (d) Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 70/146
71. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab Simulation Results of ”Office” Image
Figure: (a) Original Image (b) Histogram Equalized Image (c) Image
Enhanced using Gang et al. Method & (d) Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 71/146
72. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab Simulation Results of ”Girl” Image
Figure: (a) Original Image (b) Histogram Equalized Image (c) Image
Enhanced using Gang et al. Method & (d) Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 72/146
73. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Performance Comparison
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 73/146
74. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
ModelSim Simulation Results : Validity of RGB
Input
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 74/146
75. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
ModelSim Simulation Results : Validity of the
Enhanced Output
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 75/146
76. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RTL Top View of AGMF
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 76/146
77. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RTL Detailed View of RGB to HSV Color Space
Converter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 77/146
78. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RTL Detailed View of HSV to RGB Color Space
Converter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 78/146
79. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
FPGA Resource Utilization for RGB to HSV
Converter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 79/146
80. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
FPGA Resource Utilization for HSV to RGB
Converter
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 80/146
81. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
FPGA Resource Utilization for Adaptive Geometric
Mean Filter based Color Image Enhancement
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 81/146
82. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Summary for RGB to HSV Conversion
The timing summary for the RGB to HSV Color Space Converter as
reported by Xilinx ISE tool is as follows:
Speed Grade : -10
Minimum period: 29.730 ns (Maximum Frequency:
33.635 MHz)
Minimum input arrival time before clock: 3.971 ns
Maximum output required time after clock: 8.047 ns
Clock period: 29.730 ns (frequency: 33.635 MHz)
Total number of paths /destination ports:
158142043944387 / 824
Delay: 29.730 ns (Levels of Logic = 35)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 82/146
83. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Summary for HSV to RGB Conversion
The timing summary for the HSV to RGB Color Space Converter as
reported by Xilinx ISE tool is as follows:
Speed Grade : -10
Minimum period: 7.815 ns (Maximum Frequency:
127.958 MHz)
Minimum input arrival time before clock: 3.560 ns
Maximum output required time after clock: 9.084 ns
Clock period: 7.815 ns (frequency: 127.958 MHz)
Total number of paths /destination ports: 4867 / 727
Delay: 7.815 ns (Levels of Logic = 18)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 83/146
84. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Summary for AGMF Design
The timing summary for the AGMF Design as reported by Xilinx ISE
tool is as follows:
Speed Grade : -10
Minimum period: 4.286 ns (Maximum Frequency:
233.323 MHz)
Minimum input arrival time before clock: 10.225 ns
Maximum output required time after clock: 4.677 ns
Clock period: 4.286 ns (frequency: 233.32 MHz)
Total number of paths /destination ports: 187392 /
18456
Delay: 4.286 ns (Levels of Logic = 8)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 84/146
85. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RGB to HSV and vice versa: Software Approach
Figure: First Row : Original Image, Image in HSV Space, Restored
Image from HSV Space. Second Row :Original Image, Image in HSV
Space, Restored Image from HSV Space.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 85/146
86. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RGB to HSV and vice versa: Hardware Approach
Figure: First Row : Original Image, Image in HSV Space, Restored
Image from HSV Space. Second Row :Original Image, Image in HSV
Space, Restored Image from HSV Space.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 86/146
87. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab & Hardware Results of ”Nature” Image
Figure: First Row: Original ”Nature” Image, Photoflair Software En-
hanced Image (PSNR:28.12 dB), Image Enhanced using Histogram Equal-
ization (PSNR:29.01 dB) Second Row: Matlab Reconstructed Image us-
ing Proposed AGMF (PSNR:30.12 dB), Verilog Reconstructed Image using
Proposed AGMF (PSNR:30.98 dB)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 87/146
88. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab & Hardware Results of ”Big Ben” Image
Figure: First Row: Original ”Big Ben” Image, Photoflair Software En-
hanced Image ((PSNR:27.68 dB), Image Enhanced using Histogram Equal-
ization (PSNR: 28.3 dB) Second Row: Matlab Reconstructed Image us-
ing Proposed AGMF (PSNR:29.07 dB), Verilog Reconstructed Image using
Proposed AGMF (PSNR:29.16 dB)
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 88/146
89. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Conclusion
Adaptive color image enhancement in HSV color space
based on Geometric Mean Filter was presented.
Luminance Enhancement is achieved using Saturation feed-
back.
Geometric mean filter offers better reconstructed image
quality compared to arithmetic mean filter.
Experimental results presented shows that the color im-
ages enhanced by the proposed algorithm are clearer,
more vivid and more brilliant.
Performance of the proposed algorithm validated by ap-
plying luminance, contrast and PSNR.
The FPGA implementation of AGMF processes images of
size 1600 × 1200 pixels at 121 frames per second.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 89/146
90. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Publication Details
Adaptive Color Image Enhancement based on Geomet-
ric Mean Filter, In Proceedings of International Confer-
ence on Communication, Computing and Security (ICCCS
2011) NIT, Rourkela, India, Feb 12-14, ACM, 2011.
A Novel Reconfigurable Architecture for Enhancing Color
Image Based on Adaptive Saturation Feedback, In the In-
ternational Conference on Advanced Information and Mo-
bile Communication (AIM 2011), pp. 162-169, Nagpur,
Maharashtra, India, April 21-22, Springer, 2011.
A Novel FPGA Implementation of Adaptive Color Image
Enhancement based on HSV Color Space, In the Inter-
national Conference on Electronics and Computer Tech-
nology (ICECT 2011), pp. 162-169, Kanyakumari, India,
08-10 April, IEEE, 2011.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 90/146
91. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Gaussian Image Enhancement : Introduction
Retinex is a popular image enhancement method for bridg-
ing the gap between images and the human observation
of scenes.
Retinex Algorithm was Proposed by Edwin Herbert Land
in 1986.
Proposed Gaussian based color image enhancement tech-
nique is the modified version of NASA’s Retinex Algo-
rithm
Retinex is a model of lightness and color perception of
human vision.
Retinex is an adaptive imaging algorithm.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 91/146
92. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Gaussian Image enhancement : An Example
Figure: Original Image and Enhanced Image using Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 92/146
93. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work
D. J Jobson et al.14 proposed a color image enhancement
based on multiscale retinex for bridging the gap between
color images and Human observation of scenes.
Advantages
Good dynamic range compression and color constancy.
Reconstructed images are favorable for Human visual per-
ception and improve contrast.
Disadvantages
This method fails to produce good color rendition for a
class of images that contain violations of the gray world
assumption.
Unable to remove Halo artifacts completely.
Many parameters were assumed such as alpha, beta, gain,
Gaussian scales etc.
14
D. J Jobson, Z. Rahman and G. A Woodell, ”A Multiscale Retinex for
Bridging the Gap Between Color Images and the Human Observation of Scenes”,
in IEEE Transactions on Image Processing, Vol. 6, pp. 965-976, 1997.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 93/146
94. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Digital Signal Processors (DSPs)15 has been used for the
implementation of image enhancement algorithms.
Advantages
Improved efficiency compared to general purpose comput-
ers.
Disadvantages
Only marginal improvement has been achieved since paral-
lelism and pipelining incorporated in the design are inade-
quate.
This scheme uses optimized DSP libraries for complex op-
erations and does not take full advantage of inherent par-
allelism of image enhancement algorithm.
The enhancement of 25-30 frames per second of large size
video frames with 1024 × 768 pixel resolution is still not
possible with DSPs.
15
D. J Jobson, G. D Hines, Z. Rahman and G. A Woodell, ”DSP Implemen-
tation of the Retinex Image Enhancement Algorithm”, In Visual Information
Processing XIII, Proc. SPIE, Vol. 5438, pp. 13-24, 2004.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 94/146
95. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
The neural network based learning algorithm16 provides an
excellent solution for the color image enhancement with
color restoration.
Advantages
The hardware implementation of the algorithm parallelizes
the computation and delivers real time throughput for color
image enhancement.
Disadvantages
The window related operations such as convolution, sum-
mation and matrix dot products in an image enhancement
architecture demands an tremendous amount of hardware
resources.
16
M. Z Zhang, M. J Seow, and V. K Asari, ”A High Performance Architecture
for Color Image Enhancement using a Machine Learning Approach”, In Interna-
tional Journal of Computational Intelligence Research-Special Issue on Advances
in Neural Networks, Vol 2, Issue 1, pp. 40-47, 2006.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 95/146
96. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Hiroshi Tsutsui et al.17 proposed an FPGA implementa-
tion of adaptive real-time video image enhancement based
on variational model of the Retinex theory.
Authors claimed that the architectures developed in this
scheme are efficient and can handle color picture of size
1900 × 1200 pixels at the real time video rate of 60 fps.
However, the computational cost of the algorithm de-
pends on the number of processing layers while the maxi-
mum layers and iterations used are 5 and 30, respectively.
Also, authors have not justified how high throughput has
been achieved in spite of time consuming iterations to the
tune of 30.
17
Hiroshi Tsutsui, H. Nakamura, R. Hashimoto, H. Okuhata, and T. Onoye,
”An FPGA Implementation of Real-time Retinex Video Image Enhancement”, In
IEEE World Automation Congress (WAC), pp. 1-6. 2010.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 96/146
97. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Related Work Cont.,
Abdullah M. Alsuwailem et al.18 proposed a new approach
for HE using FPGAs.
Although efficient architectures were developed for HE,
the reconstructed images using this scheme are generally
not acceptable.
The HE process loses the details such as edges, contrast
and leads to over enhancement of noise in images.
The enhancement approach using adaptive HE scheme
also fails to produce satisfactory results since the process
enlarges the contrast of background noise while lessening
the exploitable signal.
18
Abdullah M. Alsuwailem and S. A Alshebeili, ”A New Approach for Real-
time Histogram Equalization using FPGA”, In IEEE Proceedings of International
Symposium on Intelligent Signal Processing and Communication Systems (IS-
PACS2005), pp. 397-400, 2005.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 97/146
98. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Gaussian Based Image Enhancement
Method
Original image (which is of poor quality and needing en-
hancement) is read in RGB color space.
The color components are separated followed by the con-
volution with 3×3 Gaussian kernel in order to smooth the
image.
Logarithmic operation is accomplished in order to com-
press the dynamic range of the image and to improve low
intensity pixel values.
Gain/Offset adjustment is done in order to translate the
pixels into the display range of 0 to 255.
The No. of scales, scales, gain and offset do not vary from
one image to another. This implies that the algorithm is
canonical.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 98/146
99. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Advantages of Proposed Method
Depending on circumstances, the proposed method
could achieve :
Sharpening : Compensates for the blurring introduced by
image formation process.
Color constancy processing : Improves consistency of
output as illumination changes.
Good dynamic range compression and color rendition
effect.
Canonical constant : independent of inputs.
General enhancement algorithm for all types of pictures.
Provides satisfactory results for bi-modal pictures.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 99/146
100. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Flow Diagram of Proposed Enhancement Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 100/146
101. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Gaussian Kernels: 3 × 3 kernel and 5 × 5 kernel
1
16
1 2 1
2 4 2
1 2 1
1
273
1 4 7 4 1
4 16 26 16 4
7 26 41 26 7
4 16 26 16 4
1 4 7 4 1
Figure: Gaussian Kernels: 3 × 3 kernel and 5 × 5 kernel
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 101/146
102. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Enhancement Equations
The two dimension (2D) Gaussian function is defined by
g(x, y) = 1
2πσ2 e− x2+y2
2σ2
The Gaussian convolution matrix is given by
G(x, y) = I(x, y) ⊗ g(x, y)
Mathematically, 2D convolution can be represented as
G(x, y) = M
i=1
N
j=1 I(i, j) × g(x − i, y − j)
The convolution operation for a mask of 5 × 5 is given by
P(x, y) =
4
i=0 Wi ×Pi
4
i=0 Wi
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 102/146
103. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Proposed Enhancement Equations Cont.,
The logarithmic processing on a 2D image is carried out by using
the following Eqn. GL(x, y) = K × log2 [1 + G(x, y)]
This gain/offset correction is accomplished by using Eqn. given
below:
I (x, y) = dmax
GLmax −GLmin
[GL(x, y) − GLmin]
where dmax is the maximum intensity, which is chosen as, 255 for
an image with 8-bit representation, GL(x, y) is the log-transformed
image, GLmin is the minimum value of log transformed image,
GLmax is the maximum value of log transformed image, I (x, y) is
the enhanced image and, x and y represent spatial coordinates.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 103/146
104. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Block Diagram & Signal Description for the Top
Module of Gaussian Based Image Enhancement
System
Block Diagram Signal Description
Signals Description
clk This is the global clock signal
reset n Active low system reset
rin [7:0] Red color component
gin [7:0] Green color component
bin [7:0] Blue color component
ro [7:0] Enhanced red color component
go [7:0] Enhanced Green color component
bo [7:0] Enhanced Blue color component
pixel valid Valid signal for enhanced RGB pixel
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 104/146
105. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of Gaussian Based Color
Image Enhancement System
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 105/146
106. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Top Architecture of Serpentine Memory
Signal Diagram Signal Description
Signals Description
clk Global clock signal
reset n Active low system reset
pixel in [7:0] R/G/B Input pixel
window valid Valid signal for sliding window
w11 [7:0] to w15 [7:0] First row pixel values
w21 [7:0] to w25 [7:0] Second row pixel values
w31 [7:0] to w35 [7:0] Third row pixel values
w41 [7:0] to w45 [7:0] Fourth row pixel values
w51 [7:0] to w55 [7:0] Fifth row pixel values
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 106/146
107. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of Serpentine Memory System
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 107/146
108. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Top Architecture of 2D Gaussian Convolution
Signal Diagram Signal Description
Signals Description
window valid Valid signal from sliding window
W11 to W15 First row pixel values
W21 to W25 Second row pixel values
W51 to W55 Fifth row pixel values
G11 to G15 First row Gaussian kernel values
G21 to G25 Second row Gaussian kernel values
G51 to G55 Fifth row Gaussian kernel values
conv out [7:0] Gaussian convolved output pixels
conv valid Valid signal
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 108/146
109. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of 2D Gaussian Convolution
Processor for R/G/B Color Channels
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 109/146
110. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Architectures of Adder, Multiplier and Logarithm
Adder & Multiplier Module
Logarithm Module
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 110/146
111. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of 24-bit Unsigned Adder
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 111/146
112. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Detailed Architecture of Pipelined Multiplier Design
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 112/146
113. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Results and Discussions
Simulation Tool : Matlab Ver. 7.6.0.324 (R2008a).
Resolution of Test Images : 640 × 480 Pixels (VGA),
800 × 600 Pixels (SVGA), 1024 × 768 Pixels (XGA).
Test Images :
http://dragon.larc.nasa.gov/retinex/pao/news/
http://visl.technion.ac.il/1999/99-07/www/
http://ivrg.epfl.ch/index.html
Performance Metrics : Contrast Enhancement,
Luminance Enhancement, Peak Signal to Noise Ratio
(PSNR).
Histogram : Plotted to show Pixel Distribution.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 113/146
114. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Comparison of Matlab Reconstructed Pictures Using
Image Enhancement Algorithms: ”Trees” Image
Figure: First Row:Original Image, Histogram Equalization, NASA’s
MSRCR. Second Row:Chih et al. MSRCR, Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 114/146
115. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Comparison of Matlab Reconstructed Pictures Using
Image Enhancement Algorithms: ”Palette” Image
Figure: First Row:Original Image, Histogram Equalization, NASA’s
MSRCR. Second Row:Chih et al. MSRCR, Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 115/146
116. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Comparison of Matlab Reconstructed Pictures Using
Image Enhancement Algorithms: ”House” Image
Figure: First Row:Original Image, Histogram Equalization, NASA’s
MSRCR. Second Row:Chih et al. MSRCR, Proposed Method
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 116/146
117. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
ModelSim Simulation Waveforms for Inputting
Image Data
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 117/146
118. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Waveforms of Sliding Window Module for one of the
R/G/B Color Components
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 118/146
119. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Waveforms for Gaussian Convolution Output at
21090 ns
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 119/146
120. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Starting of Enhanced Pixel Data
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 120/146
121. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Waveforms for Ending of Reconstructed Pixels at
13,31,990 ns
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 121/146
122. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Processing Time Report for the Top Design Module
of Gaussian Based Image Enhancement System
Module Clock Cycles Required to Process each pixel data
Sliding Window 1029
Gaussian convolution 23
Logarithm 1
gain/offset correction 7
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 122/146
123. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Timing Diagram for Illustrating the Pipelining
Operation of the Proposed System
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 123/146
124. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
RTL View of the ”Gaussian IE”
RTL View of the Top Module
aZoomed RTL View
a
Note: U1: Red Color,U2: Green
Color, U3: Blue Color Component Pro-
cessors
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 124/146
125. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Zoomed View of U1 or U2 or U3 Module
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 125/146
126. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
FPGA Resource Utilization for Gaussian Based Color
Image Enhancement Design
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 126/146
127. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab and Verilog Reconstructed ”Tree”, ”House”
and ”Color Palette” Images
Figure: First Column: Original Images, Second Column: Matlab
Reconstructed Images, Third Column: Hardware Reconstructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 127/146
128. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Matlab and Verilog Reconstructed ”Couple”, ”Dark
Road” and ”Memorial Church” Images
Figure: First Column: Original Images, Second Column: Matlab
Reconstructed Images, Third Column: Hardware Reconstructed Images
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 128/146
129. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Performance Evaluation : Approximate Wavelet
Energy Metric
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 129/146
130. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Performance Evaluation : Detailed Wavelet Energy
Metric
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 130/146
131. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Conclusion
Gaussian based color image enhancement algorithm was
designed and architectures were developed.
The proposed method is efficient from computation point
of view as compared to other researcher methods.
The visual quality of reconstructed pictures and image en-
hancement achieved for various test images are compared
using wavelet energy metric.
Color image enhancement system is implemented on Xil-
inx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is
capable of processing high resolution videos up to 1600 ×
1200 pixels at 117 frames per second.
RTL compliant Verilog coding of our system fits into a
single FPGA chip with a gate count utilization of about
321,804.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 131/146
132. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
AROF Architectures and Its FPGA Implementation
Adaptive Color Image Enhancement using GMF
Gaussian Image Enhancement: Algorithm & Architecture
Publication Details
Design of Novel Algorithm and Architecture for Gaussian
based Color Image Enhancement, in International Con-
ference on Advances in Computing, Communication and
Control, Mumbai, India, 18-19 Jan, 2013.
Design and FPGA Implementation of a 2D Gaussian Sur-
round Function with Reduced On-Chip Memory Utiliza-
tion, in International Conference on Advances in Com-
puting, Communication and Informatics (ICACCI2013),
Mysore, India, 22-25 Aug, 2013.
A Novel Full-Reference Color Image Quality Assessment
Based on Energy Computation in the Wavelet Domain”,
In Journal of Intelligent Systems, Vol. 22, No. 2, pp.
155-177, May 2013.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 132/146
133. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Conclusions
The AROF have been used in this work to remove impulse
noise since AROF has better filtering properties compared
to AMF.
The core modules of the AROF system, namely, sliding
window, impulse noise detection, sorting network, median
computation and output selection were realized using Ver-
ilog for ASIC/FPGA implementation.
A Novel algorithm for color image enhancement based
on AGMF is proposed with the architecture development
suitable for FPGA/ASIC implementations.
The Verilog codes for the functional modules of AGMF,
namely, RGB to HSV, histogram equalization, value com-
ponent enhancement, HSV to RGB converter etc. have
been developed and successfully simulated.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 133/146
134. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Conclusions Cont.,
Design of new algorithm and architectures for Gaussian
based color image enhancement system for real-time ap-
plications has been presented.
The Gaussian color image enhancement functional mod-
ules, namely, serpentine memory, 2D Gaussian convolu-
tion, logarithm base-2 and gain/offset correction were re-
alized using Verilog conforming to RTL coding guidelines
practised in Industries.
The designs presented exploits high degrees of pipelin-
ing and parallel processing in order to achieve real time
performance.
Quality assessment of image enhancement algorithms are
based metrics: CEP, LEP, PSNR, and WE etc.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 134/146
135. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Future Scope
The work presented throws open a number of work that
may be undertaken by researchers in the near future.
The design of the proposed AROF is modular and flexible,
and therefore, it can be upgraded to accommodate new
modules, both present and future, without appreciable
increase in hardware.
The functional modules of AROF, AGMF & Gaussian im-
age enhancement residing in FPGAs presently can be re-
placed by ASIC resulting in more compact, low power,
high speed and cost effective system suitable for volume
production.
A medical image enhancement technique based on retinex
can also be designed and implemented on FPGA/ASIC by
modifying the algorithms and architectures.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 135/146
136. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
VLSI Image Processing Groups in Foreign
Universities
Dr. Vijayan K. Asari, Old Dominion University, Norfolk, USA
http://www.ece.odu.edu/~vasari/
Dr. Ryan Kastner, University of California, Sandiego http:
//cseweb.ucsd.edu/~kastner/main
Dr. Junguk Cho, University of California, Sandiego http://
cseweb.ucsd.edu/~j10cho/index.html
Dr. Venkatesan Muthukumar, University of Nevada Las Vegas,
USA http://www.ee.unlv.edu/~venkim/index.html
Dr. Ming Z. Zhang, Old Dominion University, Norfolk, USA
http://caprolibra.com/Prfdex.html
Dr. Sudha Natarajan, NTU, Singapore http://www.ntu.edu.
sg/home/sudha/
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 136/146
137. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
VLSI Image Processing Groups in Indian Universities
Dr. Swapna Banerjee, Dept. of EE, CAD and VLSI Laboratory,
IIT Kharagpur, India.
Dr. Nitin Chandrachoodan, Dept. of EE, VLSI Laboratory, IIT
Madras, India.
Dr. S. Srinivasan, VLSI Laboratory, Dept. of EE, IIT Madras,
India.
Dr. V. Kamakoti, Reconfigurable and Intelligent Systems Engi-
neering Group (RISE Laboratory), Dept. of CSE, IIT Madras,
India.
Sanjay Sing, Scientist Fellow, IC Design Group, CEERI, Pilani,
India.
Dr. S. S. S. P Rao, Dept. of CSE, IIT Bombay, India.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 137/146
138. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
VLSI Image Processing Journals
Elsevier Journal on Microprocessors and Micro-systems.
Springer Journal of VLSI Signal Processing Systems for Signal,
Image and Video Technology.
IEEE Transactions on Very Large Scale Integration (VLSI) Sys-
tems.
IEEE Transactions on Circuits and Systems for Video Technol-
ogy.
IEEE Journal on Computer Architectures for Intelligent Ma-
chines.
Journal of Circuits, Systems and Computers.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 138/146
142. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Refrences
Mohd Firdaus Zakaria, Haidi Ibrahim and Shahrel Azmin
Suandi, ”A Review: Image Compensation Techniques”, Pro-
ceedings of Second International Conference on Computer En-
gineering and Technology (ICCET-2010), 16-18 April, 2010.
C. Iakovidou, V. Vonikakis and I. Andreadis, ”FPGA implemen-
tation of a real-time biologically inspired image enhancement
algorithm”, Journal of Real Time Image Processing, Vol. 3, No.
4, pp. 269-287, 2008.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 142/146
143. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Refrences
Ming Z. Zhanga,Ming-Jung Seowa, Li Tao and Vijayan K.
Asari,”A tunable high-performance architecture for enhance-
ment of stream video captured under non-uniform lighting con-
ditions”, Journal of Micrprocessors and Microsystems, Vol. 32,
Issue 7, pp. 386-393, 2008.
Hiroshi Tsutsui, Hideyuki Nakamura, Ryoji Hashimoto, Hi-
royuki Okuhata and Takao Onoye, ”An FPGA Implementation
of Real-Time Retinex Video Image Enhancement”, Proceedings
of World Automation Congress (WAC), pp. 1-6, 19-23 Sept,
2010.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 143/146
144. Introduction
Motivation & Objectives
Contributions
Conclusions & Future Scope
Refrences
D. J Jobson, Z. Rahman, and G. A Woodell, A Multiscale retinex
for bridging the gap between color images and the human ob-
servation of scenes, IEEE Transaction Image Processing, Vol. 6,
No. 7, pp. 965-976, July 1997.
Xinghao Ding, Xinxin Wang, Quan Xiao, ”Color Image En-
hancement with a Human Visual System based Adaptive Filter”,
Proceedings of International Conference on Image Analysis and
Signal Processing, April, 2010.
Hongqing Hu and Guoqiang Ni, ”The improved algorithm for
the defect of the Retinex Image Enhancement”, Proceedings
of International Conference on Anti-Counterfeiting Security and
Identification in Communication (ASID), pp. 257-260, July,
2010.
M. C, Hanumantharaju, Ph.D Dissertation Defense on Development of VLSI Architectures for Image Enhancement Techniques: slide 144/146