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;   }  } 

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