Anisotropic filtering of images

Anisotropic filtering of images

Anisotropy, or anisotropy, is the opposite of isotropy, and refers to the characteristics of all or part of the physical and chemical properties of an object that change with different directions. For example, the conductivity of a single crystal of graphite is The difference in different directions can reach thousands of times, and in astronomy, the cosmic microwave background radiation also has a slight anisotropy. Many physical quantities have anisotropy, such as elastic modulus, electrical conductivity, dissolution rate in acid, etc.

Anisotropic diffusion filtering is mainly used to smooth the image and overcomes the defect of Gaussian blur. Anisotropic diffusion preserves the edge of the image when smoothing the image, much like bilateral filtering.

Code:

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;
float k = 15;
float lambda = 0.25;
int N = 20;

void anisotropy_demo(Mat &image, Mat &result);
int main(int argc, char** argv) {
    Mat src = imread("D:/vcprojects/images/example.png");
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);
    vector<Mat> mv;
    vector<Mat> results;
    split(src, mv);
    for (int n = 0; n < mv.size(); n++) {
        Mat m = Mat::zeros(src.size(), CV_32FC1);
        mv[n].convertTo(m, CV_32FC1);
        results.push_back(m);
    }

    int w = src.cols;
    int h = src.rows;
    Mat copy = Mat::zeros(src.size(), CV_32FC1);
    for (int i = 0; i < N; i++) {
        anisotropy_demo(results[0], copy);
        copy.copyTo(results[0]);

        anisotropy_demo(results[1], copy);
        copy.copyTo(results[1]);

        anisotropy_demo(results[2], copy);
        copy.copyTo(results[2]);

    }
    Mat output;
    normalize(results[0], results[0], 0, 255, NORM_MINMAX);
    normalize(results[1], results[1], 0, 255, NORM_MINMAX);
    normalize(results[2], results[2], 0, 255, NORM_MINMAX);

    results[0].convertTo(mv[0], CV_8UC1);
    results[1].convertTo(mv[1], CV_8UC1);
    results[2].convertTo(mv[2], CV_8UC1);

    Mat dst;
    merge(mv, dst);

    imshow("result", dst);
    imwrite("D:/result.png", dst);
    waitKey(0);
    return 0;
}

void anisotropy_demo(Mat &image, Mat &result) {
    int width = image.cols;
    int height = image.rows;

    // 四邻域梯度
    float n = 0, s = 0, e = 0, w = 0; 
    // 四邻域系数
    float nc = 0, sc = 0, ec = 0, wc = 0; 
    float k2 = k*k;
    for (int row = 1; row < height -1; row++) {
        for (int col = 1; col < width -1; col++) {
            // gradient
            n = image.at<float>(row - 1, col) - image.at<float>(row, col);
            s = image.at<float>(row + 1, col) - image.at<float>(row, col);
            e = image.at<float>(row, col - 1) - image.at<float>(row, col);
            w = image.at<float>(row, col + 1) - image.at<float>(row, col);
            nc = exp(-n*n / k2);
            sc = exp(-s*s / k2);
            ec = exp(-e*e / k2);
            wc = exp(-w*w / k2);
            result.at<float>(row, col) = image.at<float>(row, col) + lambda*(n*nc + s*sc + e*ec + w*wc);
        }
    }
}

The effect burst: