数学基础:
二维高斯模糊的数学表达如下：
简单表达为G(x, y) = G(x)G(y)由此，对一个二维的像素矩阵可以分别在水平与垂直方向
进行一维高斯模糊其效果等同与二维高斯模糊效果。由于计算量的减少，速度却比二维
高斯模糊更快。
运行结果：
关键代码解析：
生成一维高斯核的代码如下，其中对高斯核实现归一化预处理
private float[] generateGaussianKeneral(int n, float sigma) { float sigma22 = 2*sigma*sigma; float Pi2 = 2*(float)Math.PI; float sqrtSigmaPi2 = (float)Math.sqrt(Pi2) * sigma ; int size = 2*n + 1; int index = 0; gaussianKeneral = new float[size]; float sum = 0.0f; for(int i=-n; i<=n; i++) { float distance = i*i; gaussianKeneral[index] = (float)Math.exp((-distance)/sigma22)/sqrtSigmaPi2; //System.out.println("\t" + gaussianKeneral[index]); sum += gaussianKeneral[index]; index++; } // nomalization to 1 for(int i=0; i<gaussianKeneral.length; i++) { gaussianKeneral[i] = gaussianKeneral[i]/sum; //System.out.println("final gaussian data " + i + " = " + gaussianKeneral[i]); } return gaussianKeneral; }
实现对越界像素值的处理：
public static int clamp(float a) { return (int)(a < 0 ? 0 : ((a > 255) ? 255 : a)); }
全部算法源代码如下：
package com.gloomyfish.filter.study; import java.awt.image.BufferedImage; public class HVGaussianFilter extends AbstractBufferedImageOp { private float[] gaussianKeneral = null; private int radius = 2; // default value private float sigma = 10; public HVGaussianFilter() { } public void setSigma(float a) { this.sigma = a; } public void setRadius(int size) { this.radius = size; } public float[][] getHVGaussianKeneral() { float[][] hvKeneralData = new float[5][5]; for(int i=0; i<5; i++) { for(int j=0; j<5; j++) { hvKeneralData[i][j] = gaussianKeneral[i] * gaussianKeneral[j]; } } return hvKeneralData; } @Override public BufferedImage filter(BufferedImage src, BufferedImage dest) { generateGaussianKeneral(radius, sigma); 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); blur( inPixels, outPixels, width, height); // H Gaussian blur( outPixels, inPixels, height, width); // V Gaussain setRGB(dest, 0, 0, width, height, inPixels ); return dest; } /** * <p> here is 1D Gaussian , </p> * * @param inPixels * @param outPixels * @param width * @param height */ private void blur(int[] inPixels, int[] outPixels, int width, int height) { int subCol = 0; int index = 0, index2 = 0; float redSum=0, greenSum=0, blueSum=0; for(int row=0; row<height; row++) { int ta = 0, tr = 0, tg = 0, tb = 0; index = row; for(int col=0; col<width; col++) { // index = row * width + col; redSum=0; greenSum=0; blueSum=0; for(int m=-2; m<=2; m++) { subCol = col + m; if(subCol < 0 || subCol >= width) { subCol = 0; } index2 = row * width + subCol; ta = (inPixels[index2] >> 24) & 0xff; tr = (inPixels[index2] >> 16) & 0xff; tg = (inPixels[index2] >> 8) & 0xff; tb = inPixels[index2] & 0xff; redSum += (tr * gaussianKeneral[m + 2]); greenSum += (tg * gaussianKeneral[m + 2]); blueSum += (tb * gaussianKeneral[m + 2]); } outPixels[index] = (ta << 24) | (clamp(redSum) << 16) | (clamp(greenSum) << 8) | clamp(blueSum); index += height;// correct index at here!!!, out put pixels matrix, } } } public static int clamp(float a) { return (int)(a < 0 ? 0 : ((a > 255) ? 255 : a)); } private float[] generateGaussianKeneral(int n, float sigma) { float sigma22 = 2*sigma*sigma; float Pi2 = 2*(float)Math.PI; float sqrtSigmaPi2 = (float)Math.sqrt(Pi2) * sigma ; int size = 2*n + 1; int index = 0; gaussianKeneral = new float[size]; float sum = 0.0f; for(int i=-n; i<=n; i++) { float distance = i*i; gaussianKeneral[index] = (float)Math.exp((-distance)/sigma22)/sqrtSigmaPi2; //System.out.println("\t" + gaussianKeneral[index]); sum += gaussianKeneral[index]; index++; } // nomalization to 1 for(int i=0; i<gaussianKeneral.length; i++) { gaussianKeneral[i] = gaussianKeneral[i]/sum; //System.out.println("final gaussian data " + i + " = " + gaussianKeneral[i]); } return gaussianKeneral; } }
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