图像处理之直方图均衡化
基本思想：
直方图图均衡化是图像处理中的常用图像增强手段，直方图均衡化的主要优点是
可以降低图像噪声，提升图像的局部显示。对于常见的RGB图像，直方图均衡化
可以分别在三个颜色通道上处理，基本的直方图均衡化的公式为：
其中nj表示灰度级为Rk的像素的个数，L为图像中灰度总数，对于RGB来说L的
取值范围为[0~255]总灰度级为256个。而R表示输入图像的直方图数据。根据输
出的灰度值Sk计算出输出像素的每个像素值，完成直方图均衡化之后的像素处理
程序效果：
源代码：
package com.gloomyfish.filter.study; import java.awt.image.BufferedImage; public class HistogramEFilter extends AbstractBufferedImageOp{ @Override public BufferedImage filter(BufferedImage src, BufferedImage dest) { int width = src.getWidth(); int height = src.getHeight(); if ( dest == null ) dest = createCompatibleDestImage( src, null ); int[] inPixels = new int[width*height]; int[] outPixels = new int[width*height]; getRGB( src, 0, 0, width, height, inPixels ); int[][] rgbhis = new int[3][256]; // RGB int[][] newrgbhis = new int[3][256]; // after HE for(int i=0; i<3; i++) { for(int j=0; j<256; j++) { rgbhis[i][j] = 0; newrgbhis[i][j] = 0; } } int index = 0; int totalPixelNumber = height * width; for(int row=0; row<height; row++) { int ta = 0, tr = 0, tg = 0, tb = 0; for(int col=0; col<width; col++) { index = row * width + col; ta = (inPixels[index] >> 24) & 0xff; tr = (inPixels[index] >> 16) & 0xff; tg = (inPixels[index] >> 8) & 0xff; tb = inPixels[index] & 0xff; // generate original source image RGB histogram rgbhis[0][tr]++; rgbhis[1][tg]++; rgbhis[2][tb]++; } } // generate original source image RGB histogram generateHEData(newrgbhis, rgbhis, totalPixelNumber, 256); for(int row=0; row<height; row++) { int ta = 0, tr = 0, tg = 0, tb = 0; for(int col=0; col<width; col++) { index = row * width + col; ta = (inPixels[index] >> 24) & 0xff; tr = (inPixels[index] >> 16) & 0xff; tg = (inPixels[index] >> 8) & 0xff; tb = inPixels[index] & 0xff; // get output pixel now... tr = newrgbhis[0][tr]; tg = newrgbhis[1][tg]; tb = newrgbhis[2][tb]; outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb; } } setRGB( dest, 0, 0, width, height, outPixels ); return dest; } /** * * @param newrgbhis * @param rgbhis * @param totalPixelNumber * @param grayLevel [0 ~ 255] */ private void generateHEData(int[][] newrgbhis, int[][] rgbhis, int totalPixelNumber, int grayLevel) { for(int i=0; i<grayLevel; i++) { newrgbhis[0][i] = getNewintensityRate(rgbhis[0], totalPixelNumber, i); newrgbhis[1][i] = getNewintensityRate(rgbhis[1], totalPixelNumber, i); newrgbhis[2][i] = getNewintensityRate(rgbhis[2], totalPixelNumber, i); } } private int getNewintensityRate(int[] grayHis, double totalPixelNumber, int index) { double sum = 0; for(int i=0; i<=index; i++) { sum += ((double)grayHis[i])/totalPixelNumber; } return (int)(sum * 255.0); } }
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