图像处理之双边滤波效果(Bilateral Filtering for Gray and Color Image)

图像处理之双边滤波效果(Bilateral Filtering for Gray and Color Image)

基本介绍：

普通的时空域的低通滤波器，在像素空间完成滤波以后，导致图像的边缘部分也变得不那么明显，

整张图像都变得同样的模糊，图像边缘细节丢失。双边滤波器（ABilateral Filter）可以很好的保

留边缘的同时消除噪声。双边滤波器能做到这些原因在于它不像普通的高斯/卷积低通滤波，只考

虑了位置对中心像素的影响，它还考虑了卷积核中像素与中心像素之间相似程度的影响，根据位置

影响与像素值之间的相似程度生成两个不同的权重表(WeightTable)，在计算中心像素的时候加以

考虑这两个权重，从而实现双边低通滤波。据说AdobePhotoshop的高斯磨皮功能就是应用了

双边低通滤波算法实现。

程序效果：

看我们的美女lena应用双边滤镜之后

程序关键代码解释：

建立距离高斯权重表(Weight Table)如下：

private void buildDistanceWeightTable() { 	int size = 2 * radius + 1; 	cWeightTable = new double[size][size]; 	for(int semirow = -radius; semirow <= radius; semirow++) { 		for(int semicol = - radius; semicol <= radius; semicol++) { 			// calculate Euclidean distance between center point and close pixels 			double delta = Math.sqrt(semirow * semirow + semicol * semicol)/ds; 			double deltaDelta = delta * delta; 			cWeightTable[semirow+radius][semicol+radius] = Math.exp(deltaDelta * factor); 		} 	} }

建立RGB值像素度高斯权重代码如下：

private void buildSimilarityWeightTable() { 	sWeightTable = new double[256]; // since the color scope is 0 ~ 255 	for(int i=0; i<256; i++) { 		double delta = Math.sqrt(i * i ) / rs; 		double deltaDelta = delta * delta; 		sWeightTable[i] = Math.exp(deltaDelta * factor); 	} }

完成权重和计算与像素×权重和计算代码如下：

for(int semirow = -radius; semirow <= radius; semirow++) { 	for(int semicol = - radius; semicol <= radius; semicol++) { 		if((row + semirow) >= 0 && (row + semirow) < height) { 			rowOffset = row + semirow; 		} else { 			rowOffset = 0; 		} 		 		if((semicol + col) >= 0 && (semicol + col) < width) { 			colOffset = col + semicol; 		} else { 			colOffset = 0; 		} 		index2 = rowOffset * width + colOffset; 		ta2 = (inPixels[index2] >> 24) & 0xff;         tr2 = (inPixels[index2] >> 16) & 0xff;         tg2 = (inPixels[index2] >> 8) & 0xff;         tb2 = inPixels[index2] & 0xff;                  csRedWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tr2 - tr))];         csGreenWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tg2 - tg))];         csBlueWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tb2 - tb))];                  csSumRedWeight += csRedWeight;         csSumGreenWeight += csGreenWeight;         csSumBlueWeight += csBlueWeight;         redSum += (csRedWeight * (double)tr2);         greenSum += (csGreenWeight * (double)tg2);         blueSum += (csBlueWeight * (double)tb2); 	} }

完成归一化，得到输出像素点RGB值得代码如下：

tr = (int)Math.floor(redSum / csSumRedWeight); tg = (int)Math.floor(greenSum / csSumGreenWeight); tb = (int)Math.floor(blueSum / csSumBlueWeight); outPixels[index] = (ta << 24) | (clamp(tr) << 16) | (clamp(tg) << 8) | clamp(tb);

关于什么卷积滤波，请参考：

http://blog.csdn.net/jia20003/article/details/7038938

关于高斯模糊算法，请参考：
http://blog.csdn.net/jia20003/article/details/7234741
最后想说，不给出源代码的博文不是好博文，基于Java完成的双边滤波速度有点慢

可以自己优化，双边滤镜完全源代码如下：

package com.gloomyfish.blurring.study; /**  *  A simple and important case of bilateral filtering is shift-invariant Gaussian filtering  *  refer to - http://graphics.ucsd.edu/~iman/Denoising/  *  refer to - http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html  *  thanks to cyber  */ import java.awt.image.BufferedImage;  public class BilateralFilter extends AbstractBufferedImageOp { 	private final static double factor = -0.5d; 	private double ds; // distance sigma 	private double rs; // range sigma 	private int radius; // half length of Gaussian kernel Adobe Photoshop  	private double[][] cWeightTable; 	private double[] sWeightTable; 	private int width; 	private int height; 	 	public BilateralFilter() { 		this.ds = 1.0f; 		this.rs = 1.0f; 	} 	 	private void buildDistanceWeightTable() { 		int size = 2 * radius + 1; 		cWeightTable = new double[size][size]; 		for(int semirow = -radius; semirow <= radius; semirow++) { 			for(int semicol = - radius; semicol <= radius; semicol++) { 				// calculate Euclidean distance between center point and close pixels 				double delta = Math.sqrt(semirow * semirow + semicol * semicol)/ds; 				double deltaDelta = delta * delta; 				cWeightTable[semirow+radius][semicol+radius] = Math.exp(deltaDelta * factor); 			} 		} 	} 	 	/** 	 * for gray image 	 * @param row 	 * @param col 	 * @param inPixels 	 */ 	private void buildSimilarityWeightTable() { 		sWeightTable = new double[256]; // since the color scope is 0 ~ 255 		for(int i=0; i<256; i++) { 			double delta = Math.sqrt(i * i ) / rs; 			double deltaDelta = delta * delta; 			sWeightTable[i] = Math.exp(deltaDelta * factor); 		} 	} 	 	public void setDistanceSigma(double ds) { 		this.ds = ds; 	} 	 	public void setRangeSigma(double rs) { 		this.rs = rs; 	}  	@Override 	public BufferedImage filter(BufferedImage src, BufferedImage dest) { 		width = src.getWidth();         height = src.getHeight();         //int sigmaMax = (int)Math.max(ds, rs);         //radius = (int)Math.ceil(2 * sigmaMax);         radius = (int)Math.max(ds, rs);         buildDistanceWeightTable();         buildSimilarityWeightTable();         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 index = 0; 		double redSum = 0, greenSum = 0, blueSum = 0; 		double csRedWeight = 0, csGreenWeight = 0, csBlueWeight = 0; 		double csSumRedWeight = 0, csSumGreenWeight = 0, csSumBlueWeight = 0;         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;                 int rowOffset = 0, colOffset = 0;                 int index2 = 0;                 int ta2 = 0, tr2 = 0, tg2 = 0, tb2 = 0;         		for(int semirow = -radius; semirow <= radius; semirow++) {         			for(int semicol = - radius; semicol <= radius; semicol++) {         				if((row + semirow) >= 0 && (row + semirow) < height) {         					rowOffset = row + semirow;         				} else {         					rowOffset = 0;         				}         				         				if((semicol + col) >= 0 && (semicol + col) < width) {         					colOffset = col + semicol;         				} else {         					colOffset = 0;         				}         				index2 = rowOffset * width + colOffset;         				ta2 = (inPixels[index2] >> 24) & 0xff;         		        tr2 = (inPixels[index2] >> 16) & 0xff;         		        tg2 = (inPixels[index2] >> 8) & 0xff;         		        tb2 = inPixels[index2] & 0xff;         		                 		        csRedWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tr2 - tr))];         		        csGreenWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tg2 - tg))];         		        csBlueWeight = cWeightTable[semirow+radius][semicol+radius]  * sWeightTable[(Math.abs(tb2 - tb))];         		                 		        csSumRedWeight += csRedWeight;         		        csSumGreenWeight += csGreenWeight;         		        csSumBlueWeight += csBlueWeight;         		        redSum += (csRedWeight * (double)tr2);         		        greenSum += (csGreenWeight * (double)tg2);         		        blueSum += (csBlueWeight * (double)tb2);         			}         		}         		 				tr = (int)Math.floor(redSum / csSumRedWeight); 				tg = (int)Math.floor(greenSum / csSumGreenWeight); 				tb = (int)Math.floor(blueSum / csSumBlueWeight); 				outPixels[index] = (ta << 24) | (clamp(tr) << 16) | (clamp(tg) << 8) | clamp(tb);                                  // clean value for next time...                 redSum = greenSum = blueSum = 0;                 csRedWeight = csGreenWeight = csBlueWeight = 0;                 csSumRedWeight = csSumGreenWeight = csSumBlueWeight = 0;                          	}         }         setRGB( dest, 0, 0, width, height, outPixels );         return dest; 	} 	 	public static int clamp(int p) { 		return p < 0 ? 0 : ((p > 255) ? 255 : p); 	}  	public static void main(String[] args) { 		BilateralFilter bf = new BilateralFilter(); 		bf.buildSimilarityWeightTable(); 	} } 

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