图像处理之添加高斯与泊松噪声

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：
/article/1391113.html
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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