OpenCV Tutorials —— Histogram Equalization

直方图均衡化 —— 其潜在的数学原理是一个分布（输入的亮度直方图）被映射到另一个分布

其目的是拉伸原始图像直方图，增强其对比度

• To accomplish the equalization effect, the remapping should be the cumulative distribution function (cdf) (more details, refer to Learning OpenCV). For the histogram , its cumulative distribution is:

  累计分布函数作为映射函数，计算的过程中需要归一化直方图

  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 ). From the example above, the cumulative function is:

  

• Finally, we use a simple remapping procedure to obtain the intensity values of the equalized image:

  

Code

 

#include "stdafx.h"

#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;

	char* source_window = "Source image";
	char* equalized_window = "Equalized Image";

	/// Load image
	src = imread( "test1.jpg", 1 );

	if( !src.data )
	{ cout<<"Usage: ./Histogram_Demo <path_to_image>"<<endl;
	return -1;}

	/// Convert to grayscale
	cvtColor( src, src, CV_BGR2GRAY );

	/// Apply Histogram Equalization
	equalizeHist( src, dst );	// 全封装进去了 ~~

	/// 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;
}

直方图均衡化 —— 频谱被展开

对于彩色图像，必须先将每个通道分开，再分别进行处理

适用于直方图分布过于集中（对比不明显的）图像