Weitere ähnliche Inhalte Ähnlich wie digital image processing, image processing (20) Mehr von Kalyan Acharjya (20) Kürzlich hochgeladen (20) digital image processing, image processing2. A Lecture on
Introduction to
DIGITAL IMAGE PROCESSING
24-09-2013©Kalyan Acharjya
2
Presented By
Kalyan Acharjya
Assistant Professor, Dept. of ECE
Jaipur National University
3. 25-09-2013©Kalyan Acharjya 3
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Kalyan Acharjya
5. Contents
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What is image and Image
Processing?
Image Understanding.
Why Digital image Processing ?
Digitization of Image.
Histogram and Thresholding of
Image.
Noise and its Extraction from
Image.
Edge Detection.
Image Enhancement.
Image Compression.
Data Hiding in Image.
Color Image Processing.
Applications
7. What is image?
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An image is a 2-D light intensity function f( x, y).
An image is considered as Matrix.
A digital image f( x, y) is described both in
spatial co-ordinates and Brightness.
• The points in the image and element value of matrix
identifies gray level value at that point.
This element is called pels or Pixels.
• So f( x, y)=R( x, y) *I(x, y)
Where R Reflectivity of Surface (Pixel Point)
I Intensity of incident Light
y
x
8. Matrix or Digital Representation of Image
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• An Image has infinite intensity value.
• Also infinite picture point -How its stored.
• Digitization of image.
Spatial discretization by Sampling.
Intensity discretization by Quantization.
I=
9. Matrix as an Image
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An Matrix is an image for DIP
10. Types of Digital Images
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Binary Image: Each Pixel is just
Black and white, i.e. 0 or 1
<462x493 logical>
Gray Scale Image: Each Pixel is
shade of Gray, its 0(black) to
white(255),i.e. each pixel~8 bits
<462x493 unit8>
Color Image or RGB Image, Each
pixel corresponds to 3 values.
<462x493x3 unit8>
11. Image Data Type
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int8-8 bit integer, Range -128 to 127
unit8-8 bit unsigned integer, Range 0 to 225
int16-16 bit integer, Range -32768 to 32767
uint16-16 bit unsigned integer, Range 0 to 65535
double-Double precision real number, Machine Specific
12. Levels of Image Understanding
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Low Level-Involve primitive operations.
e.g. Image Preprocessing, noise reduction,
Enhancement etc.
Image input - Image Out
• Mid Level-
Image segmentation, identify particular objects.
Image input - Attributes extracted from those images
e.g. edges, contour, identify etc.
• High Level-Involving making sense of an ensemble of recognize objects, image
analysis and far end the functions normally associated with human vision.
Image
• Processing
Image
• Analysis
Image
• Measurements
14. History of Image Processing
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Its not young field, In 1920 submarine cables were used to transmit digitalized
newspaper pictures between London and New-Work-Use Telegraphic Printing.
In 1921 –Improved in printing, use photographic
printing to enhance the quality and resolution.
Actually DIP/ Computer Processing Technique
was used to improve the pictures of moon
transmitted by RANGER 7 at JET PROPULSION LAB.
it’s the real beginning…
16. Why Digital Image Processing
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How we stored the image: Reduce the size for storage .
How analog image world is relate to digital processing world.
Compression-Remove redundancies.
Transmission with minimum bandwidth.
Lossy Compression=redundancy +some information, but still acceptable.
Original Image
Size-116 KB
Compressed Image
Size-12.9 KB, 11 %
Compressed Image
Size-1.95 KB, 1.6 %
17. Why Digital Image Processing
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Image Enhancement :To improve the
interpretability or perception of
information in image.
Spatial Domain Method.
Frequency Domain Method.
• Moving Object Tracking
• Human-Computer Interaction
• Computer Vision etc.
Lena Central Compressed
Spatial Domain Frequency Domain
19. Lets, little detail : Digitization of Image
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a b
a b
a b a b
Fig-A-Continuous Image Fig-B-Gray level Variation from a to b
Fig-C-Sampling and Quantization Fig-D-Digital line from a to b
20. Bit Planes of Grey Scale image
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Grey scale images can be transformed into a sequence of binary images by
breaking them into bit planes.
21. Spatial and Gray Level Resolution
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• Gray level L=2k
• L is discrete level allowed to
each pixel.
• M and N are spatial
• Halve and Double
• The number of bits required to
store digital image b=MxNxk
• When M=N, b= kN2
23. Arithmetic and Logical Operations in images
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Say Image as y=f(x)
This include add or subtract or multiply or
divide each pixel value by constant factor,
which may be pixel value of another image.
Y=f(x)+/-/*c
Complement: For gray scale image is its
photographic negative.
Logical Operations: AND,OR,NOT in binary
image.
26. Histograms of Images
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Histograms of Gray level image represents the numbers of times each gray
level occurs in the image.
Dark image-the gray levels would be clustered at the lower end
In a Uniformly bright image, the gray levels would be clustered at the
upper end.
In a well contrasted image, the gray levels would be well spread out over
much of the range.
27. Importance of Histograms Graph
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In Poorly Contrast image, enhance by spreading out of its histograms.
There are two ways-
Histograms stretching (contrast Stretching).
Histogram Equalization.
Histograms stretching:
• Poorly contrasted image in the range [a, b]
• Stretch the gray levels in the center of the range out by applying a
piecewise linear function.
• This function has the effect of stretching the gray levels [a, b] to
[c, d], where a<c and d>b
28. Histograms stretching (Cont.)
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The linear function imadjust(I, [a, b],[c, d])
if Pixel value is less than c are all converted to c and pixel values greater
than d are all converted to d.
a b 1
c
d
1
Gamma<1 Gamma>1
Y= (
𝑥−𝑎
𝑏−𝑎
)^Gamma (d-c)+c
30. Histograms Equalization
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Histogram equalization is a technique for adjusting image intensities to
enhance contrast
• Histogram equalisation algorithm: Let be the
intensities of the image, and let be its normalised histogram
function. The intensity transformation function for histogram equalisation is
That is, we add the values of the normalised histogram function from 1
to k to find where the intensity will be mapped. Notice that the range
of the equalised image is the interval [0,1].
mkrk ,...,2,1,
)( krp
k
j
kk rprT
1
)()(
kr
32. Thresholding of an image
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Single Threshold:
A gray scale image is turned into a binary image by first choosing a
gray level T in the original image.
Pixel Value>T tends to white (1)
Pixel Value<=T tends to black (0)
• Double Threshold:
A pixel becomes white if T1<pixel value<T2.
A pixel becomes black if gray level is others.
34. Noise and Its Extraction
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Noise is any degradation in the image signal caused by external
disturbance.
Salt and pepper noise: It is caused by sharp and sudden disturbances in
the image.
Gaussian noise: It is caused by random fluctuations in the signal. It can be
idealized form of white noise.
Speckle noise: it is modeled by random values multiplied by pixel values. In
radar applications.
Shot noise: The dominant noise in the lighter parts of an image from
an image sensor.
35. Removal of noise by Filtering
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Linear filter
Median Filter
Specific case of order statistic filtering.
Remove salt & pepper noise.
Adaptive Filter
Weiner filter use to remove Gaussian
noise.
36. Extract Noise from an image
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Image with Gaussian Noise Image after Noise removal
‘wiener2’ Filter
37. Spectral Filtering
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Spectral filtering is most commonly used to either select or eliminate
information from an image based on the wavelength of the information.
Spectral selectivity is a technique for creating images which uses
intentionally limited ranges of radiation in the ultraviolet, visible or
infrared portions of the spectrum
38. Example High Pass Filtering
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OriginalImage
HighPassfilteringresult
Highfrequency
emphasisresult
Afterhistogram
equalisation
40. What is Image Edge
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Edges are those places in an image that
correspond to object boundaries.
Edges are pixels where image brightness
changes abruptly.
It is a vector variable (magnitude of the
gradient, direction of an edge) .
41. Steps of Edge Detection
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Filtering – Filter image to improve performance of the Edge Detector with
respect to noise
Enhancement – Emphasize pixels having significant change in local intensity
Detection – Identify edges - Thresholding
Localization – Locate the edge accurately, estimate edge orientation
Types of Edges
Step Edge
Ramp Edge
Line Edge
Roof Edge
43. Edge Detection
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Motivation: Detect changes in the pixel value as large gradient.
I(m , n)={
1 𝑔 𝑚, 𝑛 > 𝑇ℎ
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Image x(m , n) Edge Map I(m, n)
Prewitt Operator.
Sobel Operator.
Canny Edge Detector.
Kirsch Compass Masks.
Roberts Operator
Gradient
Operator
Thresholding
44. Basics Relationship Between Pixels
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Neighbors of pixel
Adjacency, Connectivity
4 Adjacency.
8 Adjacency.
m Adjacency.
Image Operations
Point
Local
Global
Region and Boundaries.
Distance between Pixel
Image operation on pixel basis.
46. Image Enhancement Technique
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Basic Gray level transformation
Histogram Modification
Average and Median Filtering
Frequency domain operations.
Homomorphic Filtering.
Edge Enhancement.
Image
Enhancement
Technique
Better Image
Spatial or Frequency Domain
47. Spatial Domain
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Point of interest is f( x, y)
Contrast stretching
All these point operation, hence its point processing.
f(x, y)
y
x
r
s
r > Input gray level
s > Output Gray level
s=T(r)
s=T(r)
s
r
Threshold
48. Image Enhancement in Frequency Domain
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• To filter an image in the frequency domain:
Compute F( u, v) the DFT of the image
Multiply F( u , v) by a filter function H( u, v)
Compute the inverse DFT of the result
49. DFT of Image
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• The DFT of a two dimensional image can be visualised by showing the
spectrum of the images component frequencies.
DFT
y
x
v
u
51. Ex. of Image Enhancement in FD
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• Different low pass Gaussian filters used to remove blemishes in a photograph.
52. Frequency Domain Laplacian Example
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Original Image
Laplacian Filtered
Image
Laplacian Image
Scaled
Enhanced image
53. Conclusion of Filtering
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Fourier transform in Image Processing in the frequency domain
Image smoothing
Image sharpening
Fast Fourier Transform
Image restoration using the spatial and frequency based techniques.
55. Information Hiding by Image Processing
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Steganography
It is the process of hiding of a secret message within an ordinary image.
• Watermarking
It is the process of hiding of a secret message within an ordinary image, but
carrier image must be unchanged.
Encoder Decoder
57. Image Compression-Lossy or Lossless
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Image compression is the process of reducing the amount of data required
to represent an image.
But its resolution or features should be unchanged for human perception.
Relative Data Redundancy Rd of the first data set is Rd=1-1/CR
where CR-Compression Ratio=n1/n2 ,n1 and n2 denote the nos. of information
carrying units in two data sets that represent the same information.
• In Digital Image Compression , the basics data redundancies are
Coding Redundancy
Inter pixel Redundancy
Psycho-visual Redundancy
58. Image Compression General Models
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Some image Compression Standard
JPEG-Based on DCT
JPEG 2000-Based on DWT
GIF-Graphics Interchange Format etc.
Source
Encoder
Channel
Encoder
Channel
Decoder
Source
Decoder
Channel/
Store
F(x, y)
F’(x, y)
59. Data ≠ Information
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Data and information are not synonymous terms!
Data is the means by which information is conveyed.
Data compression aims to reduce the amount of data required to represent
a given quantity of information while preserving as much information as
possible.
Image compression is an irreversible process.
Some Transform used for Image Compression
DCT-Discrete Cosine Transform
DWT-Discrete wavelet Transform etc
61. Color Image Processing
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• RGB : Color Monitor, Color Camera, Color Scanner
• CMY : Color Printer, Color Copier
• YIQ : Color TV-Y(Luminance), I(In phase), Q(Quadrature)
HSI, HSV
62. Color Issue of an Image
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Red, Green and Blue Color cube
Consider Each element=8 bit
R,G,B ~0 to 255
Grey scale f(x , y , L)
256 Grey shades
Color Scale f(x , y, r , g , b)-24 bit
255x255x255=16777216 colors
63. What a color image contains
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65. CMY and CMYK Color Model
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Cyan(C), Magenta(M) and Yellow(Y) are the secondary colors of light.
• Or CMY are Primary colors of pigments.
RGB to CMY
Black=Cyan + Magenta + Yellow
Printing Industry used to four color Printing.
Cyan, Magenta, Yellow plus Black.
B
G
R
Y
M
C
1
1
1
67. Popular Image Processing Software Tools
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CVIP tools
(Computer Vision and Image Processing tools)
Intel Open Computer Vision Library
Microsoft Vision SDL Library
MATLAB
KHOROS
69. Applications of Digital Image Processing
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Identification.
Robot vision.
Steganography.
Image Enhancement.
Image Analysis in Medical.
Morphological Image Analysis.
Space Image Analysis.
IC Industry……….etc.