Module 1.2: Histogram Equalization: Solved Numerical Example 2 | Digital Image Processing
📊 Mastering Histogram Equalizations: Step-by-Step Tutorial! 📊
In this video, I have discussed step by step procedure to solve numerical question based on Histogram Equalization.
Histogram: Histogram is a discrete function formed by counting the number of pixels that have a certain gray level in the image
In the dark images, components of the histogram are concentrated on the lower (dark) side of the gray scale. In bright images, the histogram is biased towards the higher side of the gray scale
In low contrast, the histogram will be narrow & centered towards the middle of the gray scale. In high contrast, a large variety of gray tones occupy the entire range of possible gray levels
Histogram Equalization:
1. It is the process that transforms the intensity values so that the histogram of the output image approximately matches the flat (uniform) histogram.
2. The aim is to create an image with equally distributed brightness levels over the whole brightness scale.
3. We can do it by adjusting the probability density function (pdf) of the original histogram of the image so that the probability spread equally.
4. Histogram equalization results are similar to contrast stretching but offer the advantage of full automation
5. Histogram Equalization automatically determines a transformation function to produce a new image with a uniform histogram.
You may refer the following books to practice more numerical questions:
1. R.C.Gonzalez and R.E.Woods, “Digital Image Processing”, Prentice Hall, 3rd Edition,2011.
2. S. Sridhar , “Digital Image Processing”, Oxford University Press,2011
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