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();
}
}

感觉不错，请顶一下！

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