C++编写代码实现“空域平滑”、“空域锐化”、“高斯低通滤波”、“高斯高通滤波”、“巴特沃斯低通滤波”和“巴特沃斯高通滤波”等功能

以下是C++编写代码实现空域平滑、空域锐化、高斯低通滤波、高斯高通滤波、巴特沃斯低通滤波和巴特沃斯高通滤波的功能的例子：

1. 空域平滑（均值滤波）

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat smoothed;
    blur(image, smoothed, Size(3, 3));
    imshow("Smoothed Image", smoothed);
    waitKey(0);
    return 0;
}

2. 空域锐化（拉普拉斯滤波）

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat sharpened;
    Mat blurred;
    GaussianBlur(image, blurred, Size(3, 3), 0);
    addWeighted(image, 1.5, blurred, -0.5, 0, sharpened);
    imshow("Sharpened Image", sharpened);
    waitKey(0);
    return 0;
}

3. 高斯低通滤波

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat filtered;
    GaussianBlur(image, filtered, Size(3, 3), 0);
    imshow("Filtered Image", filtered);
    waitKey(0);
    return 0;
}

4. 高斯高通滤波

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat filtered;
    Mat blurred;
    GaussianBlur(image, blurred, Size(3, 3), 0);
    subtract(image, blurred, filtered);
    imshow("Filtered Image", filtered);
    waitKey(0);
    return 0;
}

5. 巴特沃斯低通滤波

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat filtered;
    Mat padded;
    int dft_M = getOptimalDFTSize(image.rows);
    int dft_N = getOptimalDFTSize(image.cols);
    copyMakeBorder(image, padded, 0, dft_M - image.rows, 0, dft_N - image.cols, BORDER_CONSTANT, Scalar::all(0));
    Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
    Mat complexI;
    merge(planes, 2, complexI);
    dft(complexI, complexI);
    float cutoff_frequency = 30.0;
    float order = 2.0;
    float d0 = cutoff_frequency * dft_M / 100.0;
    for (int i = 0; i < complexI.rows; i++) {
        for (int j = 0; j < complexI.cols; j++) {
            float d = sqrt(pow(i - complexI.rows / 2, 2) + pow(j - complexI.cols / 2, 2));
            float h = 1.0 / (1.0 + pow(d / d0, 2 * order));
            complexI.at<Vec2f>(i, j)[0] *= h;
            complexI.at<Vec2f>(i, j)[1] *= h;
        }
    }
    idft(complexI, filtered, DFT_SCALE | DFT_REAL_OUTPUT);
    filtered = filtered(Rect(0, 0, image.cols, image.rows));
    imshow("Filtered Image", filtered);
    waitKey(0);
    return 0;
}

6. 巴特沃斯高通滤波

#include <opencv2/opencv.hpp>

using namespace cv;

int main() {
    Mat image = imread("input.jpg");
    Mat filtered;
    Mat padded;
    int dft_M = getOptimalDFTSize(image.rows);
    int dft_N = getOptimalDFTSize(image.cols);
    copyMakeBorder(image, padded, 0, dft_M - image.rows, 0, dft_N - image.cols, BORDER_CONSTANT, Scalar::all(0));
    Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
    Mat complexI;
    merge(planes, 2, complexI);
    dft(complexI, complexI);
    float cutoff_frequency = 30.0;
    float order = 2.0;
    float d0 = cutoff_frequency * dft_M / 100.0;
    for (int i = 0; i < complexI.rows; i++) {
        for (int j = 0; j < complexI.cols; j++) {
            float d = sqrt(pow(i - complexI.rows / 2, 2) + pow(j - complexI.cols / 2, 2));
            float h = 1.0 / (1.0 + pow(d0 / d, 2 * order));
            complexI.at<Vec2f>(i, j)[0] *= h;
            complexI.at<Vec2f>(i, j)[1] *= h;
        }
    }
    idft(complexI, filtered, DFT_SCALE | DFT_REAL_OUTPUT);
    filtered = filtered(Rect(0, 0, image.cols, image.rows));
    imshow("Filtered Image", filtered);
    waitKey(0);
    return 0;
}