数学基础：
什么是泊松噪声，就是噪声分布符合泊松分布模型。泊松分布(Poisson Di)的公
式如下：
关于泊松分布的详细解释看这里：http://zh.wikipedia.org/wiki/泊松分佈
关于高斯分布与高斯噪声看这里：
http://blog.csdn.net/jia20003/article/details/7181463
二:程序实现
以前在图像加噪博文中现实的加高斯噪声，比较复杂。是自己完全实现了高斯随
机数的产生，这里主要是利用JAVA的随机数API提供的nextGaussion()方法来得
到高斯随机数。泊松噪声为了简化计算，Google到一位神人完成的C++代码于是
我翻译成Java的。
三：程序效果
滤镜源代码：
- package com.gloomyfish.filter.study;
- import java.awt.image.BufferedImage;
- import java.util.Random;
- public class NoiseAdditionFilter extends AbstractBufferedImageOp {
- public final static double MEAN_FACTOR = 2.0;
- public final static int POISSON_NOISE_TYPE = 2;
- public final static int GAUSSION_NOISE_TYPE = 1;
- private double _mNoiseFactor = 25;
- private int _mNoiseType = POISSON_NOISE_TYPE;
- public NoiseAdditionFilter() {
- System.out.println("Adding Poisson/Gaussion Noise");
- }
- public void setNoise(double power) {
- this._mNoiseFactor = power;
- }
- public void setNoiseType(int type) {
- this._mNoiseType = type;
- }
- @Override
- public BufferedImage filter(BufferedImage src, BufferedImage dest) {
- int width = src.getWidth();
- int height = src.getHeight();
- Random random = new Random();
- 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;
- 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;
- if(_mNoiseType == POISSON_NOISE_TYPE) {
- tr = clamp(addPNoise(tr, random));
- tg = clamp(addPNoise(tg, random));
- tb = clamp(addPNoise(tb, random));
- } else if(_mNoiseType == GAUSSION_NOISE_TYPE) {
- tr = clamp(addGNoise(tr, random));
- tg = clamp(addGNoise(tg, random));
- tb = clamp(addGNoise(tb, random));
- }
- outPixels[index] = (ta << 24) | (tr << 16) | (tg << 8) | tb;
- }
- }
- setRGB( dest, 0, 0, width, height, outPixels );
- return dest;
- }
- private int addGNoise(int tr, Random random) {
- int v, ran;
- boolean inRange = false;
- do {
- ran = (int)Math.round(random.nextGaussian()*_mNoiseFactor);
- v = tr + ran;
- // check whether it is valid single channel value
- inRange = (v>=0 && v<=255);
- if (inRange) tr = v;
- } while (!inRange);
- return tr;
- }
- public static int clamp(int p) {
- return p > 255 ? 255 : (p < 0 ? 0 : p);
- }
- private int addPNoise(int pixel, Random random) {
- // init:
- double L = Math.exp(-_mNoiseFactor * MEAN_FACTOR);
- int k = 0;
- double p = 1;
- do {
- k++;
- // Generate uniform random number u in [0,1] and let p ← p × u.
- p *= random.nextDouble();
- } while (p >= L);
- double retValue = Math.max((pixel + (k - 1) / MEAN_FACTOR - _mNoiseFactor), 0);
- return (int)retValue;
- }
- }
