直方图均衡化（matlab实现）

对图像（灰度图）进行直方图均衡化主要有一下几个步骤：

1、计算各个灰度值（0-255）出现的次数

2、计算各个灰度值的累积分布率

2、根据累积分布率计算出原来各灰度值的均衡化之后的新的值

%This file is to implement histogram equlizationoriginalImage = imread('tire.tif');%这个TIFF图像是四维的（M*N*4），需要转换成灰度图%先取出TIFF图像的前三维数据（丢弃第四维）并将其合成RGB图像R = originalImage(:,:,1);G = originalImage(:,:,2);B = originalImage(:,:,3);rgbImage = cat(3,R,G,B);grayImage = rgb2gray(rgbImage);%显示图像figure,imshow(grayImage);title('Original Image');%计算图像的像素个数numOfPixels = size(grayImage,1) * size(grayImage,2);frequency = zeros(256,1);%用于统计各个灰度出现的次数probability = zeros(256,1);%计算出现的可能性for i=1:size(grayImage,1)    for j=1:size(grayImage,2)       value = grayImage(i,j);       frequency(value+1) = frequency(value+1) + 1;       probability(value+1) = frequency(value+1) / numOfPixels;    endendcum = zeros(256,1);%记录各个像素值的累积分布probc = zeros(256,1);%记录各个像素值的累积分布的可能性output = zeros(256,1);%与那图像中各个灰度值的均衡化之后的值sum = 0;numOfBins = 255;for i=1:size(probability)    sum = sum + frequency(i);    cum(i) = sum;    probc(i) = cum(i) / numOfPixels;    output(i) = round(probc(i) * numOfBins);endHEImage = uint8(zeros(size(grayImage,1),size(grayImage,2)));%均衡化后的图像for i=1:size(grayImage,1)    for j=1:size(grayImage,2)        HEImage(i,j) = output(grayImage(i,j)+1);    endendfigure,imshow(HEImage);title('Histogram Equalization');

结果：