1. Lecture 3 (2.5.07) Image Enhancement in Spatial Domain
Shahram Ebadollahi
11/29/2018
DIP ELEN E4830

2. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

3. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

4. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

5. Processing Images in Spatial Domain: Introduction
: Spatial operator defined on a neighborhood N of a given pixel
point processing
mask processing
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6. Mask (filter, kernel, window, template) processing
(0,0) y
(0,0) y x x
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7. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

8. Image Histogram normalized histogram histogram H bi-level image 255
0.5
bi-level image
255
256x256 H
Pixel values linearly increasing from 0 to 255 with increasing column index
histogram
1/256
255
256x256
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9. Image Histogram: example
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10. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

11. Point Processing: Thresholding
Input gray-level value
Output gray-level value
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12. Point Processing: Gamma Correction
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13. Point Processing: Contrast Stretching
L-1
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14. Point Processing: Clipping & Thresholding
L-1
clipping
L-1
thresholding
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15. Point Processing: Gray-level Slicing
L-1
L-1
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16. Point Processing: Bit-plane Slicing
lsb
msb
where,
e.g.
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17. Point Processing: Bit-plane Slicing (example)
Point operation for obtaining n-th bit-plane:
Bi-level image
n=7
n=6
n=5
n=4
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18. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

19. Histogram Modification
Apply a transform to an image such that the resulting image has desired histogram.
Histogram Equalization (linearization)
Histogram Specification (matching)
Non-adaptive vs. Adaptive Histogram Modification
Global histogram
Local histogram
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20. Histogram Equalization
Source image
Equalized Image
Corresponding Histograms
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21. Histogram Equalization
Often images poorly use the full range of the gray scale
Solution:
Transform image such that its histogram is spread out more evenly in gray scale
Rather than guessing the parameters and the form of the transformation use original gray-scale distribution as the cue
11/29/2018

22. Histogram Equalization
# pixels with the j-th gray-level
Point operation for equalizing histogram for the example image
image size
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23. Histogram Matching
Transform image such that resulting image has specified histogram
Histogram Matching
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24. Histogram Matching
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25. Adaptive Histogram Equalization
(0,0) y
Histogram Equalization
Note: local structure revealed x
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26. Today’s Lecture - Outline
Review of Lecture 2
Processing Images in Spatial Domain: Intro
Image Histogram
Point Operations
Using Histogram for Image Enhancement
Kernel Operations
11/29/2018

27. Kernel Operator: Intro
Note: need to handle borders of the image
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28. Kernel Operator: Intro
Spatial Filtering
kernel
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29. Smoothing: Image AveragingFT
Low-pass filter *
Image edges are softened
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30. Smoothing: Averaging (example)
original
3x3
5x5
9x9
Noise effect is gone, but image edges are heavily blurred also
15x15
35x35
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31. Order Statistics Filter
original
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32. Image Derivative
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33. Image Sharpening: 1-st derivative
Image gradient:
Robert’s operator
Sobel filter in frequency domain
Sobel’s operator
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34. Image Sharpening: 2-nd derivative
Image Laplacian:
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35. Image Sharpening: 2-nd derivative+ *
Laplacian filter in frequency domain
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36. High-boost Filtering - + + Avg. Unsharp mask: high-boost with A=1
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37. Recap Point operations vs. Kernel Operations Image Histogram
Image Enhancement using Point Operators
Contrast Stretching
Gamma Correction
Using Image Histogram for Enhancement
Histogram Equalization
Histogram Matching
Image Enhancement using Kernel Operators
Low-pass filtering (averaging)
High-pass filtering (sharpening)
11/29/2018