滤镜之高斯噪声GaussianNoise

首先看这两张效果图：

 

图像加上高斯噪声非常简单了，每个像素点叠加上高斯噪声即可。同理也可以叠加上其他的噪声。具体可以参考GIMP代码

 

算法原理：

1.     首先随机产生高斯噪声，可以是RGB同一个随机噪声，也可以是三通道不同的随机噪声。

2.     将随机噪声的值叠加到像素中，并判断是否越界。

 

该算法存在一个参数：

1.     噪声程度：越大则噪声越大，图像越脏

高斯噪声产生的代码是从The Science Of FractalImages一书中得来的，GIMP中也是采用类似代码：

/* * Return a Gaussian (aka normal) random variable. * * Adapted from ppmforge.c, which is part of PBMPLUS. * The algorithm comes from: * 'The Science Of Fractal Images'. Peitgen, H.-O., and Saupe, D. eds. * Springer Verlag, New York, 1988. */static intgauss(int scale){    double sum;    sum = (RANDOM() & 0x7FFF) + (RANDOM() & 0x7FFF) +(RANDOM() & 0x7FFF) + (RANDOM() & 0x7FFF);    return (int) (scale * (sum * 5.28596089837e-5 - 3.46410161514));}

算法代码：没有经过任何优化

int GenGauss(int nLevel){double   d = (rand() + rand() + rand() + rand()) * 5.28596089837e-5 - 3.46410161514 ;return (int)(nLevel * d * 127.0 / 100.0) ;}void GuassianNoisyRGB(unsigned char* pInput,unsigned char* pOutput,int width,int height,int nStride,int nLevel){int bRandom = 0;int     n1, n2, n3 ;int i,j;int temp,index;if(nLevel<0)nLevel = 0;if(nLevel > 100)nLevel = 100;if(pInput == NULL || pOutput == NULL)return;if(width <= 0 || height <= 0)return;srand ((unsigned int)time(0)) ;for (j=0;j<height;j++){for (i=0;i<width;i++){if (bRandom){n1=GenGauss(nLevel);n2=GenGauss(nLevel);n3=GenGauss(nLevel);}else{n1=n2=n3=GenGauss(nLevel) ;}index = j*nStride+i*3;temp = pInput[index] + n1;if(temp<0)temp = 0;if(temp>255)temp = 255; pOutput[index] = temp;temp = pInput[index+1] + n2;if(temp<0)temp = 0;   if(temp>255)temp = 255; pOutput[index+1] = temp;temp = pInput[index+2] + n3;if(temp<0)temp = 0;if(temp>255)temp = 255; pOutput[index+2] = temp;}}}