opencv c++通过傅里叶变换去除正弦噪声

傅里叶变换可以将信号从时域转换到频域，因此可以用于去除正弦噪声。以下是使用OpenCV C++实现去除正弦噪声的示例代码：

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

using namespace std;
using namespace cv;

int main()
{
    Mat img = imread("lena.jpg", IMREAD_GRAYSCALE);
    if (img.empty())
    {
        cout << "Failed to read image!" << endl;
        return -1;
    }

    // Add sinusoidal noise
    int amplitude = 30;
    double frequency = 0.05;
    for (int i = 0; i < img.rows; i++)
    {
        for (int j = 0; j < img.cols; j++)
        {
            int value = img.at<uchar>(i, j) + amplitude * sin(2 * CV_PI * frequency * j);
            img.at<uchar>(i, j) = saturate_cast<uchar>(value);
        }
    }

    // Perform Fourier transform
    Mat imgFreq;
    dft(img, imgFreq, DFT_COMPLEX_OUTPUT);

    // Shift the zero-frequency component to the center of the spectrum
    Mat imgFreqShifted;
    imgFreqShifted = imgFreq.clone();
    int cx = imgFreqShifted.cols / 2;
    int cy = imgFreqShifted.rows / 2;
    Mat q0(imgFreqShifted, Rect(0, 0, cx, cy));
    Mat q1(imgFreqShifted, Rect(cx, 0, cx, cy));
    Mat q2(imgFreqShifted, Rect(0, cy, cx, cy));
    Mat q3(imgFreqShifted, Rect(cx, cy, cx, cy));
    Mat tmp;
    q0.copyTo(tmp);
    q3.copyTo(q0);
    tmp.copyTo(q3);
    q1.copyTo(tmp);
    q2.copyTo(q1);
    tmp.copyTo(q2);

    // Create a mask to filter out the sinusoidal noise
    Mat mask(img.size(), CV_32FC1, Scalar(0));
    int radius = 20;
    Point center(img.cols / 2, img.rows / 2);
    circle(mask, center, radius, Scalar(1), -1);

    // Apply the mask to the frequency spectrum
    Mat imgFreqFiltered;
    multiply(imgFreqShifted, mask, imgFreqFiltered);

    // Perform inverse Fourier transform
    Mat imgFiltered;
    idft(imgFreqFiltered, imgFiltered, DFT_SCALE | DFT_REAL_OUTPUT);

    // Show the results
    namedWindow("Original", WINDOW_AUTOSIZE);
    imshow("Original", img);
    namedWindow("Noisy", WINDOW_AUTOSIZE);
    imshow("Noisy", imgFreqShifted);
    namedWindow("Filtered", WINDOW_AUTOSIZE);
    imshow("Filtered", imgFiltered);

    waitKey(0);
    return 0;
}

该代码首先将lena图像加载为灰度图像，然后添加正弦噪声。接下来，它执行傅里叶变换，并将零频率分量移动到频谱的中心。然后，它创建一个掩膜，以过滤掉正弦噪声。通过将掩膜应用于频谱，可以得到过滤后的频谱。最后，它执行逆傅里叶变换以恢复图像，并显示原始图像、噪声图像和过滤后的图像。