Image Enhancement method and its necessity

1. Image Enhancement
Subject: Image Procesing & Computer Vision
Dr. Varun Kumar
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 1 / 12

2. Outlines
1 Necessity of Image Enhancement
2 Spatial Domain Operation
Point Processing
Histogram Based Techniques
Mask Processing
3 Frequency Domain Operation
4 References
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 2 / 12

3. Introduction to image enhancement
Image enhancement
⇒ It is a process of enhancing the certain features of an image.
⇒ The result is more suitable than the original one for certain
application.
Processing techniques are very much problem oriented.
Best technique for enhancement of X-ray image may not be the best
for microscopic image.
Broad category of image enhancement
1 Spatial domain technique
Work on image plane itself
Direct manipulation of pixels in an image
2 Frequency domain technique
Modify Fourier coefficient of an image.
Take inverse Fourier transform for enhanced image.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 3 / 12

4. Continued–
Basic operation
⇒ g(x) = T[f (x)]
⇒ g(x, y) = T[f (x, y)]
Neighborhood operation:
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 4 / 12

5. Point processing
⇒ Operator operates on single point.
⇒ The basic operation for point processing can be expressed as
s = T(r)
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 5 / 12

6. Masking operation
3 × 3 neighborhood
Mathematical representation
g(x, y) =
1
i=−1
1
j=−1
wi,j f (x + i, y + j) (1)
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 6 / 12

7. Image negative
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 7 / 12

8. Contrast stretching
Problem associated with object perception:
⇒ Due to poor illumination of the respective image.
⇒ Due to small dynamic range of the sensor for imaging.
⇒ More details available in the processed image rather than original
image.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 8 / 12

9. Dynamic range compression
Dynamic range compression is used to map the natural dynamic range
of a signal to a smaller range.
This is achieved by modifying the illumination component of the
image.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 9 / 12

10. Power law transformation
s = T(r) = crγ
Note : For proper image visualization, there is need for correction of γ
value in power law transformation is called as γ correction.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 10 / 12

11. Continued–
Gamma correction
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 11 / 12

12. References
M. Sonka, V. Hlavac, and R. Boyle, Image processing, analysis, and machine vision.
Cengage Learning, 2014.
D. A. Forsyth and J. Ponce, “A modern approach,” Computer vision: a modern
approach, vol. 17, pp. 21–48, 2003.
L. Shapiro and G. Stockman, “Computer vision prentice hall,” Inc., New Jersey,
2001.
R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using
MATLAB. Pearson Education India, 2004.
Subject: Image Procesing & Computer Vision Dr. Varun Kumar (IIIT Surat)Lecture 17 12 / 12