測試環境：vs2012+opencv2.4.10

#include <stdio.h>
#include <opencv2/core/core.hpp>
#include <opencv2/core/core.hpp>//cv::Mat是一個n維矩陣類
#include <opencv2/highgui/highgui.hpp>//提供輸入輸出介面
#include <opencv2/imgproc/imgproc.hpp>//影象處理
#include "iostream"

using namespace cv;
using namespace std;

//mu高斯函式的偏移，sigma高斯函式的標準差
double generateGaussianNoise(double mu, double sigma)
{
	//定義小值，numeric_limits<double>::min()是函式,返回編譯器允許的double型數最小值
	const double epsilon = std::numeric_limits<double>::min();
	static double z0, z1;
	static bool flag = false;
	flag = !flag;
	//flag為假構造高斯隨機變數x
	if(!flag) return z1 * sigma + mu;
	//構造隨機變數
	double u1, u2;
	do
	{
		u1 = rand() * (1.0 / RAND_MAX);
		u2 = rand() * (1.0 / RAND_MAX);
	} while (u1 <= epsilon);
	//flag為真構造高斯隨機變數x
	z0 = sqrt(-2.0 * log(u1)) * cos(2 * CV_PI * u2);
	z1 = sqrt(-2.0 * log(u1)) * sin(2 * CV_PI * u2);
	return z0 * sigma + mu;
}
 
Mat addGaussianNoise(cv::Mat &image)
{
	cv::Mat result = image.clone();
	int channels = image.channels();
	int rows = image.rows, cols = image.cols * image.channels();
	//判斷影象連續性
	if (result.isContinuous()) cols = rows * cols, rows = 1;
	for (int i = 0; i < rows; i++)
	{
		for (int j = 0; j < cols; j++)
		{
			//新增高斯噪聲
			int val = result.ptr<uchar>(i)[j] + generateGaussianNoise(2, 0.8) * 32;
			if (val < 0) val = 0;
			if (val > 255) val = 255;
			result.ptr<uchar>(i)[j] = (uchar)val;
		}
	}
	return result;
}
int main()
{
	Mat src=imread("lena512color.jpg");
	
	Mat img = src.clone();
	Mat img1 = src.clone();

	imshow("原圖",src);
		
	Mat sobelx;
	Sobel(src, sobelx, CV_32F, 1, 0);
	imshow("Sobel的結果",sobelx);
 
	Mat dst=addGaussianNoise(src); //加入高斯噪聲
	imshow("高斯噪聲影象",dst);
	medianBlur(src,dst,3);	//中值濾波，3*3模板內排序並求中值取代原畫素
	imshow("中值濾波結果",dst);
 
	Mat dst1=addGaussianNoise(src); //加入高斯噪聲
	blur(img,dst1,Size(3,3));//均值濾波，3*3模板內求取中間值取代原畫素
	imshow("均值濾波結果",dst1);
 
	Mat dst2=addGaussianNoise(src); //加入高斯噪聲
	GaussianBlur( img1, dst2, Size( 3, 3 ), 0, 0 );//高斯濾波,
	imshow("高斯濾波結果",dst2);
 
	Rect r( 0, 0, 100, 100);
	//img = Scalar(50);//將影象img的畫素賦值為50
	Mat smallImg = img(r);//擷取顯示img影象中形狀為r的部分影象
	imshow("截圖顯示結果",smallImg);
 
	waitKey(NULL);//無限等待
	return EXIT_SUCCESS;
}


 

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