图像处理------Mean Shift滤波(边缘保留的低通滤波)

一：Mean Shift算法介绍

Mean Shift是一种聚类算法，在数据挖掘，图像提取，视频对象跟踪中都有应用。本文

重要演示Mean Shift算法来实现图像的低通边缘保留滤波效果。其处理以后的图像有点

类似油画一样。Mean Shift算法的输入参数一般有三个：

1. 矩阵半径r，声明大小

2. 像素距离，常见为欧几里德距离或者曼哈顿距离

3. 像素差值value

算法大致的流程如下：

a. 输入像素点P(x, y)

b. 计算该点的像素值pixelv

c. 根据输入的半径r与差值value求出矩阵半径内满足差值像素平均值作为输出像素点值

d. 计算shift与repetition，如果满足条件

e. 继续c ~ d，直到条件不满足退出，得到最终的输出像素值

f. 对输入图像的每个像素重复a ~ e，得到图像输出像素数据

二：色彩空间转换

本文Mean Shift滤波在YIQ颜色空间上完成，关于RGB与YIQ颜色空间转换可以参考

这里：http://en.wikipedia.org/wiki/YIQ我google找来的转换公式截屏：

图像处理------Mean Shift滤波(边缘保留的低通滤波)

三：程序效果

图像处理------Mean Shift滤波(边缘保留的低通滤波)

滤镜源代码：

[java] view plain copy

package com.gloomyfish.filter.study;
import java.awt.image.BufferedImage;
public class MeanShiftFilter extends AbstractBufferedImageOp {
private int radius;
private float colorDistance;
public MeanShiftFilter() {
radius = 3; // default shift radius
colorDistance = 25; // default color distance
}
public int getRadius() {
return radius;
}
public void setRadius(int radius) {
this.radius = radius;
}
public float getColorDistance() {
return colorDistance;
}
public void setColorDistance(float colorDistance) {
this.colorDistance = colorDistance;
}
@Override
public BufferedImage filter(BufferedImage src, BufferedImage dest) {
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);
// convert RGB color space to YIQ color space
float[][] pixelsf = new float[width*height][3];
for(int i=0; i<inPixels.length; i++) {
int argb = inPixels[i];
int r = (argb >> 16) & 0xff;
int g = (argb >> 8) & 0xff;
int b = (argb) & 0xff;
pixelsf[i][0] = 0.299f *r + 0.587f *g + 0.114f *b; // Y
pixelsf[i][1] = 0.5957f *r - 0.2744f*g - 0.3212f *b; // I
pixelsf[i][2] = 0.2114f *r - 0.5226f*g + 0.3111f *b; // Q
}
int index = 0;
float shift = 0;
float repetition = 0;
float radius2 = radius * radius;
float dis2 = colorDistance * colorDistance;
for(int row=0; row<height; row++) {
int ta = 255, tr = 0, tg = 0, tb = 0;
for(int col=0; col<width; col++) {
int xc = col;
int yc = row;
int xcOld, ycOld;
float YcOld, IcOld, QcOld;
index = row*width + col;
float[] yiq = pixelsf[index];
float Yc = yiq[0];
float Ic = yiq[1];
float Qc = yiq[2];
repetition = 0;
do {
xcOld = xc;
ycOld = yc;
YcOld = Yc;
IcOld = Ic;
QcOld = Qc;
float mx = 0;
float my = 0;
float mY = 0;
float mI = 0;
float mQ = 0;
int num=0;
for (int ry=-radius; ry <= radius; ry++) {
int y2 = yc + ry;
if (y2 >= 0 && y2 < height) {
for (int rx=-radius; rx <= radius; rx++) {
int x2 = xc + rx;
if (x2 >= 0 && x2 < width) {
if (ry*ry + rx*rx <= radius2) {
yiq = pixelsf[y2*width + x2];
float Y2 = yiq[0];
float I2 = yiq[1];
float Q2 = yiq[2];
float dY = Yc - Y2;
float dI = Ic - I2;
float dQ = Qc - Q2;
if (dY*dY+dI*dI+dQ*dQ <= dis2) {
mx += x2;
my += y2;
mY += Y2;
mI += I2;
mQ += Q2;
num++;
}
}
}
}
}
}
float num_ = 1f/num;
Yc = mY*num_;
Ic = mI*num_;
Qc = mQ*num_;
xc = (int) (mx*num_+0.5);
yc = (int) (my*num_+0.5);
int dx = xc-xcOld;
int dy = yc-ycOld;
float dY = Yc-YcOld;
float dI = Ic-IcOld;
float dQ = Qc-QcOld;
shift = dx*dx+dy*dy+dY*dY+dI*dI+dQ*dQ;
repetition++;
}
while (shift > 3 && repetition < 100);
tr = (int)(Yc + 0.9563f*Ic + 0.6210f*Qc);
tg = (int)(Yc - 0.2721f*Ic - 0.6473f*Qc);
tb = (int)(Yc - 1.1070f*Ic + 1.7046f*Qc);
outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
}
}
setRGB( dest, 0, 0, width, height, outPixels );
return dest;
}
public String toString() {
System.out.println("Mean Shift Filter...");
return "MeanShiftFilter";
}
}