图像处理------添加高斯与泊松噪声 分类： 视频图像处理 2015-07-24 14:58 61人阅读 评论(0) 收藏

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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