Histogram Equalization
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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.
Histogram Equalization
- 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)
- 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