图像处理之双边滤波效果(Bilateral Filtering for Gray and Color Image)
- created by gloomyfish
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);
}
}
}
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：
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);
}
}
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);
}
}
}
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> 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();
}
}