What is Histogram
The histogram in the context of image processing is the operation by which the occurrences of each intensity value in the image is shown. Normally, the histogram is a graph showing the number of pixels in an image at each different intensity value found in that image. For an 8-bit grayscale image there are 256 different possible intensities, and so the histogram will graphically display 256 numbers showing the distribution of pixels amongst those grayscale values. More about the histogram can be found in the Histogram/Normalized Histogram Operation article.
What is Histogram Equalization
Histogram equalization is the technique by which the dynamic range of the histogram of an image is increased. Histogram equalization assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities. It improves contrast and the goal of histogram equalization is to obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image.
Histogram equalization redistributes intensity distributions. If the histogram of any image has many peaks and valleys, it will still have peaks and valley after equalization, but peaks and valley will be shifted. Because of this, “spreading” is a better term than “flattening” to describe histogram equalization. In histogram equalization, each pixel is assigned a new intensity value based on the its previous intensity level.
The histogram equalization is operated on an image in three step:
1). Histogram Formation
2). New Intensity Values calculation for each Intensity Levels
3). Replace the previous Intensity values with the new intensity values
For the first step see the article on histogram.
In step 2, new intensity values are calculated for each intensity level by applying the following equation:
The meaning of Max. Intensity Levels maximum intensity level which a pixel can get. For example, if the image is in the grayscale domain, then the count is 255. And if the image is of size 256×256 then, the No. of pixels is 65536. And the expression is the bracket means that the no. of pixels having the intensity below the output intensity level or equal to it. For example, if we are calculating the output intensity level for 1 input intensity level, then the it means that the no. of pixels in the image having the intensity below or equal to 1 means 0 and 1. If we are calculating the output intensity level for 5 input intensity level, then the it means that the no. of pixels in the image having the intensity below or equal to 5 means 0 , 1 , 2 , 3 , 4 , 5. Thus, if we are calculating the output intensity level for 255 input intensity level, then the it means that the no. of pixels in the image having the intensity below or equal to 255 means 0 , 1 , 2 , 3 , …… , 255. That is how new intensity levels are calculated for the previous intensity levels.
The next step is to replace the previous intensity level with the new intensity level. This is accomplished by putting the value of Oi in the image for all the pixels, where Oi represents the new intensity value, whereas i represents the previous intensity level.
Guidelines for Use
To understand the working of the histogram equalization, take the example of the following image:
the dynamic range of image intensities is shown by the following histogram:
when the histogram equalization operation is performed on this image, the effects can be shown by the following equalized histogram:
and the following image shows the achieved image:
The interface for the sample project for the histogram equalization is the same as for the histogram. So, the readers are encouraged to read the Histogram/Normalized Histogram Operation article, before they start to read this article.
Project Files: HistogramEquailzation.zip