图像加噪去噪_opencv_双边滤波方法_加噪去噪_中值滤波_图像处理_

//给图像添加高斯噪声 #include <iostream> #include <string> #include <cmath> #include <limits> #include <cstdlib> #include <opencv2\core\core.hpp> #include <opencv2\highgui\highgui.hpp> #include <opencv2\imgproc.hpp> using namespace cv; using namespace std; double generateGaussianNoise(double m, double sigma); Mat addGaussianNoise(Mat& srcImage); int denoise(Mat& srcImage) { Mat dstImage; cout << "1.方框滤波" << endl; cout << "2.均值滤波" << endl; cout << "3.高斯滤波" << endl; cout << "4.中值滤波" << endl; cout << "5.双边滤波" << endl; cout << "0.返回" << endl; int num_2; int ksize; while (1) { cin >> num_2; switch (num_2) { case 1: { cout << "1.方框滤波" << endl; cout << "请输入平滑处理内核的大小（通常为正奇数1、3、5...）: "; cin >> ksize; boxFilter(srcImage, dstImage, -1, Size(ksize, ksize)); imshow("添加高斯白噪声的图像", srcImage); imshow("去噪后的图像", dstImage); waitKey(0); break; } case 2: { cout << "2.均值滤波" << endl; cout << "请输入平滑处理内核的大小（通常为正奇数1、3、5...）: "; cin >> ksize; blur(srcImage, dstImage, Size(ksize, ksize)); imshow("添加高斯白噪声的图像", srcImage); imshow("去噪后的图像", dstImage); waitKey(0); break; } case 3: { cout << "3.高斯滤波" << endl; cout << "请输入平滑处理内核的大小（通常为正奇数1、3、5...）: "; cin >> ksize; GaussianBlur(srcImage, dstImage, Size(ksize, ksize), 0, 0); imshow("添加高斯白噪声的图像", srcImage); imshow("去噪后的图像", dstImage); waitKey(0); break; } case 4: { cout << "4.中值滤波" << endl; cout << "请输入平滑处理内核的大小（通常为正奇数1、3、5...）: "; cin >> ksize; medianBlur(srcImage, dstImage, ksize); imshow("添加高斯白噪声的图像", srcImage); imshow("去噪后的图像", dstImage); waitKey(0); break; } case 5: { cout << "5.双边滤波" << endl; cout << "请输入滤波像素邻域直径（通常为正奇数1、3、5...）: "; cin >> ksize; bilateralFilter(srcImage, dstImage, ksize, ksize * 2, ksize / 2); imshow("添加高斯白噪声的图像", srcImage); imshow("去噪后的图像", dstImage); waitKey(0); break; } case 0: { cout << "1.加噪" << endl; cout << "2.去噪" << endl; cout << "0.退出" << endl; return 0; break; } default: cout << "输入错误，重新输入" << endl; break; } } waitKey(0); return 0; } int main() { string filename_src = "sudoku.png"; string filename_srcnoise = "sudoku_noise.png"; Mat srcImage = imread(filename_src); if (!srcImage.data) { cout << "读入图片错误！" << endl; system("pause"); return -1; } cout << "1.加噪" << endl; cout << "2.去噪" << endl; cout << "0.退出" << endl; int num; while (1) { cin >> num; switch (num) { case 1: { imshow("原图像", srcImage); Mat dstImage = addGaussianNoise(srcImage); imshow("添加高斯噪声后的图像", dstImage); if (!imwrite(filename_srcnoise, dstImage)) { cout << "写入图片错误！" << endl; system("pause"); } waitKey(0); break; } case 2: { Mat srcnoiseImage = imread(filename_srcnoise); if (!srcnoiseImage.data) { cout << "读入图片错误！" << endl; system("pause"); return -1; } denoise(srcnoiseImage); waitKey(0); break; } case 0: { return 0; break; } default: cout << "输入错误，重新输入" << endl; break; } } waitKey(0); return 0; } //生成高斯噪声 double generateGaussianNoise(double mu, double sigma) { //定义小值 const double epsilon = numeric_limits<double>::min(); static double z0, z1; static bool flag = false; flag = !flag; //flag为假构造高斯随机变量X if (!flag) return z1 * sigma + mu; double u1, u2; //构造随机变量 do { u1 = rand() * (1.0 / RAND_MAX); u2 = rand() * (1.0 / RAND_MAX); } while (u1 <= epsilon); //flag为真构造高斯随机变量 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; } //为图像添加高斯噪声 Mat addGaussianNoise(Mat & srcImage) { Mat dstImage = srcImage.clone(); int channels = dstImage.channels(); int rowsNumber = dstImage.rows; int colsNumber = dstImage.cols * channels; //判断图像的连续性 if (dstImage.isContinuous()) { colsNumber *= rowsNumber; rowsNumber = 1; } for (int i = 0; i < rowsNumber; i++) { for (int j = 0; j < colsNumber; j++) { //添加高斯噪声 int val = dstImage.ptr<uchar>(i)[j] + generateGaussianNoise(2, 0.8) * 32; if (val < 0) val = 0; if (val > 255) val = 255; dstImage.ptr<uchar>(i)[j] = (uchar)val; } } return dstImage; }