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 pixelsdouble 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 ~ 255for(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 sigmaprivate double rs; // range sigmaprivate 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 pixelsdouble 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 ~ 255for(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;}@Overridepublic 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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