A low cost hardware architecture for illumination adjustment in real-time applications

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A LOW-COST HARDWARE ARCHITECTURE FOR ILLUMINATION
ADJUSTMENT IN REAL-TIME APPLICATIONS
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “A Low-Cost Hardware Architecture for Illumination
Adjustment in Real-Time Applications” is the bonafide work of Mr. _____________Who
carried out the research under my supervision Certified further, that to the best of my knowledge
the work reported herein does not form part of any other project report or dissertation on the
basis of which a degree or award was conferred on an earlier occasion on this or any other
candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “A Low-Cost Hardware Architecture for
Illumination Adjustment in Real-Time Applications” Submitted to BHARATHIDASAN
UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER
OF APPLIED ELECTRONICS is a record of original work done by me the guidance of
Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here
is not a part of any other thesis or work on the basis of which a degree or award was conferred on
an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “A Low-Cost Hardware Architecture for
Illumination Adjustment in Real-Time Applications” which is a part of my curriculum of
third semester Master of Science in Computer science. I take this opportunity to express my
sincere gratitude to those who helped me in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
For real-time surveillance and safety applications in intelligent transportation systems, high-
speed processing for image enhancement is necessary and must be considered. In this paper, we
propose a fast and efﬁcient illumination adjustment algorithm that is suitable for low-cost very
large scale integration implementation. Experimental results show that the proposed method
requires the least number of operations and achieves comparable visual quality as compared with
previous techniques. To further meet the requirement of real-time image/video applications, the
16-stage pipelined hardware architecture of our method is implemented as an intellectual
property core. Our design yields a processing rate of about 200 MHz by using TSMC 0.13-μm
technology. Since it can process one pixel per clock cycle, for an image with a resolution of
QSXGA (2560 × 2048), it requires about 27 ms to process one frame that is suitable for real-time
applications. In some low-cost intelligent imaging systems, the processing rate can be slowed
down, and our hardware core can run at very low power consumption.

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INTRODUCTION:
Nowadays, there is an increasing demand for cameras and intelligent surveillance systems iming
at monitoring private and public areas. For example, the camera-based advanced driver ssistance
system can inform drivers of the appropriate speed or help keep the vehicle between lane
markers to improve vehicle/road safety. In intelligent transportation systems (ITSs), the cameras
keep track of the road and driving situations to detect the traffic flow or to record information
related to vehicle crashes or accidents automatically.
However,insufficient visibility due to the influence of weather or poor atmospheric light will
cause the intelligent system to be inoperative. Many image preprocessing algorithms are
integrated Manuscript received July 1, 2013; revised November 8, 2013, March 15, 2014, and
July 16, 2014; accepted July 28, 2014. Date of publication September 16, 2014; date of current
version March 27, 2015. This work was supported in part by the National Science Council of
Taiwan under Grant NSC-101-2221-E- 006-151-MY3, by the Ministry of Economic Affairs
(MOEA) of Taiwan under Grant MOEA 102-EC-17-A-05-S1-192, and by the Headquarters of
University Advancement at National Cheng Kung University, which is sponsored by the
Ministry of Education, Taiwan.
The Associate Editor for this paper was P. Grisleri. Y.-H. Shiau is with the Department of
Electrical Engineering, National Yunlin University of Science and Technology, Yunlin 64002,
Taiwan P.-Y. Chen, H.-Y. Yang, and S.-Y. Li are with the Digital IC Design Laboratory,
Department of Computer Science and Information Engineering, National Cheng Kung
University, Tainan 70101, Taiwan Color versions of one or more of the figures in this paper are
available online Digital Object Identifier 10.1109/TITS.2014.2347701 to increase the visibility
of the system, such as denoising,scaling, and illumination adjustment. In such a real-time
intelligent system, high-speed processing for those enhancement algorithms is necessary and
must be considered.
A hardware implementation for the algorithms [1]–[4] is a better solution, which can be included
in end-user camera equipment to meet the requirement. In this paper, we focus on the
development of a fast and efficient illumination adjustment algorithm that is suitable for low-

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cost high-speed hardware implementation. Dynamic range is the illumination ratio between the
darkest and the brightest region in a scene. A captured image in surveillances shows an
irrevocable loss of visual information in some places under strong background illumination or in
dark environments. This is mainly because the dynamic range of natural scene is far larger than
the dynamic range of image captured by the common digital devices. Such overflow will cause a
blurred image particularly in the low-luminance regions, and the viewer cannot obtain enough
information of the image.
To overcome this annoying problem, the existing enhancement methods ,for a single image are
proposed and can be classified into two categories: spatial-domain processing and compression-
domain processing .The spatial-domain processing can further be divided into two subgroups:
the histogram-based techniques and the Retinex-based techniques To consider the hardware
implementation to meet the real-time requirement, the above image enhancement algorithms
suffered from some problems. The histogrambased techniques are effective contrast
enhancement algorithms due to their straightforward and intuitive implementation qualities.
However, the histogram-based techniques need to scan the whole image twice to complete the
enhancement process, i.e., one for calculating the probability density function and one for
obtaining the enhanced image by using the mapping function.
They need to store the whole image inside the hardware core that requires a great amount of
hardware resource. Longer processing time is needed since it requires reading the whole image
data twice from the memory module. The Retinex-based techniques and compression-domain
processing techniques usually keep the details of the original image and generate high-quality
enhanced images with low noise. However, the algorithms with high time complexity are those
that will make the hardware cost high and increase the processing time. In this paper, we propose
a low-complexity illumination adjustment algorithm based on the Retinex theory.
The fast illumination estimation by using the concept of bidimensional empirical mode
decomposition (FIEEMD) is presented. Then, a modified gamma correction is employed to
adjust the illumination component to obtain a more pleasing image. The experimental results
demonstrate that our method achieves far shorter execution time and comparable visual quality

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as compared with other enhancement methods ,Moreover, we also present a 16-stage pipelined
hardware architecture implemented as an intellectual property (IP) core that can be integrated
with other circuits in the system-on-chip (SoC). By using hardware sharing and parallel
processing techniques, the proposed IP core can efﬁciently operate to meet the requirement of
real-time applications with lower cost. In some low-cost imaging systems, the processing rate
can be slowed down, and our hardware core can run at very low power consumption. The rest of
this paper is organized as follows. In Section II, the background information and the main idea
are brieﬂy introduced. Then, the proposed illumination adjustment method is presented in
Section III. Section IV describes the hardware architecture of the proposed method in detail.
Section V illustrates the simulation results and hardware implementation. Finally, the
conclusions are provided in Section VI.

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CONCLUSION:
In ITSs, the cameras keep track of the road and driving situations to detect the traffic flow or to
record information related to vehicle crashes or accidents automatically. Insufficient visibility
due to the influence of weather or poor atmospheric light will cause the intelligent system to be
inoperative. Thus, many image preprocessing algorithms are integrated into ITSs for enhancing
the visibility of the system, such as denoising, contrast enhancement, and illumination
adjustment. The enhanced image will increase the successful rate of the following processing
steps of the ITS, such as car license plate detection and recognition, human detection and
tracking, and vehicle crash or accident detection. Since the ITS is a real-time system, its high-
speed processing for enhancement algorithms is necessary and must be considered. Many
methods are proposed to reach higher visual image quality without considering the
computational complexity.
However, when it comes to implementing those algorithms on hardware core employed in the
end-user camera equipment, the computational complexity becomes an important issue,
particularly for real-time applications. In this paper, a low-complexity pipelined hardware
architecture for a fast illumination adjustment method has been proposed. The experimental
results demonstrate that our design requires the least computation load and achieves comparable
performance in objective metrics and subjective image quality as compared with other image
enhancement methods.
To our knowledge, there are few literatures on the hardware design for illumination adjustment.
Our low-cost circuit is a good solution for low-cost VLSI implementation for illumination
adjustment, which can be integrated with other hardware modules in the SOC system for real-
time transportation applications.The design meets the requirement of real-time image/video
applications and is suitable to be employed in end-user camera equipment. In the future, the
illumination adjustment method may be integrated with a haze detection and a haze removal
method. Such integration will increase the recognition rate of the following object recognition of
the ITS in bad weather conditions.

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REFERENCES:
[1] S. Marsi and G. Ramponi, “A flexible FPGA implementation for illuminance–reflectance
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pp. 1369–1374, Aug. 2013.
[3] R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A variational framework for
retinex,” Int. J. Comput. Vis., vol. 52, no. 1, pp. 7–23, Apr. 2003.
[4] C.-T. Shen and W.-L. Hwang, “Color image enhancement using retinex with robust
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A low cost hardware architecture for illumination adjustment in real-time applications

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A low cost hardware architecture for illumination adjustment in real-time applications