Histogram Equalization in OpenCV
What is an Image Histogram
- It is a graphical representation of the intensity distribution of an image.
- It quantifies the number of pixels for each intensity value considered.
What is Histogram Equalization
It is a method that improves the contrast in an image, in order to stretch out the intensity range.
How does it work
- Equalization implies mapping one distribution (the given histogram) to another distribution (a wider and more uniform distribution of intensity values) so the intensity values are spreaded over the whole range.
- To accomplish the equalization effect, the remapping should be the cumulative distribution function (cdf). For the histogram
, its cumulative distribution 
is:
To use this as a remapping function, we have to normalize such that the maximum value is 255 ( or the maximum value for the intensity of the image ). - Finally, we use a simple remapping procedure to obtain the intensity values of the equalized image:
Example
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace cv;
using namespace std;
/** @function main */
int main( int argc, char** argv )
{
Mat src, dst;
IplImage* newImg;
/* load an image named "desert.jpg", 1 means
this is a color image */
newImg = cvLoadImage("Desert.jpg",1);
char* source_window = "Source image";
char* equalized_window = "Equalized Image";
/// Load image
src = cvarrToMat(newImg);
/// Convert to grayscale
cvtColor( src, src, CV_BGR2GRAY );
/// Apply Histogram Equalization
equalizeHist( src, dst );
/// Display results
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
namedWindow( equalized_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
imshow( equalized_window, dst );
/// Wait until user exits the program
waitKey(0);
return 0;
}
Output
