This document discusses image histogram equalization. It begins by defining an image histogram as a graphical representation of the number of pixels at each intensity value. Histogram equalization automatically determines a transformation function to produce a new image with a uniform histogram and increased contrast. This technique works by mapping the intensity values of the input image to a new range of values such that the histogram of the output image is uniform. The document provides an example of performing histogram equalization on an image and assigns related homework on digital image processing applications.

1. Unit 3
Image Enhancement
Image Histogram Equalization
Lecture By
Kalyan Acharjya
kalyan5.blogspot.in
Lecture No 11

2. Histogram
 It is a plot of frequency of occurrence of an
event.

3. Histogram
 An image histogram is a graphical
representation between number of pixels and
intensity values. It plots the number of pixels vs.
each intensity value.

4. Draw the Image Histogram

5. Why Histogram?
Indentifying the
contrast of a image
Numbers of Objects
Thresholding
Segmentation

6. Histogram Processing
 Types of processing:
 Histogram equalization
 Histogram matching (specification)
 Local enhancement

7. Histogram Processing
 The histogram of a digital image with gray
levels from 0 to L-1 is a discrete function
h(rk)=nk, where:
 rk is the kth gray level
 nk is the no pixels in the image with that gray level
 n is the total number of pixels in the image
 k = 0, 1, 2, …, L-1
 Normalized histogram: p(rk)=nk/n
 Sum of all components = 1

8. Histogram Equalization
 Histogram equalization (HE) results are similar to
contrast stretching but offer the advantage of full
automation.
 Since HE automatically determines a transformation
function to produce a new image with a uniform
histogram.
)()(
0 0
k
k
k
k
k
k
k
kk rp
n
n
rTs   


9. Results after Histogram Equalization

10. Result after Histogram Equalizations

11. How and why does it work ?

12. Perform the Histogram Equalization on the
following Image
*This numerical copied from The Educational Channel (You Tube)

13. Histogram Equalization

14. Assignment
Online Submission Due Date 30 Sept 2018
1. Comments on Role of Digital Image Processing in
Modern Imaging Based Medical Treatments.
2. Explain your view on Importance of Image
Understanding in Recent Computer Vision Applications
Mid Term 1 Marks and Assignment Submission links are available at
kalyan5.blogspot.in
OR
You can directly visit at http://bit.do/dipr_jnu

15. Any Questions?
Thank You