opencv 小波去噪 C++

下面是一个使用OpenCV和C++进行小波去噪的示例代码：

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/photo/photo.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/video/video.hpp>
#include <opencv2/imgcodecs/imgcodecs.hpp>
#include <opencv2/videoio/videoio.hpp>
#include <opencv2/core/utils/logger.hpp>
#include <opencv2/core/types.hpp>
#include <opencv2/core/mat.hpp>
#include <opencv2/core/types_c.h>
#include <opencv2/core/core_c.h>
#include <opencv2/core/cvdef.h>
#include <opencv2/core/hal/interface.h>
#include <opencv2/core/hal/hal.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/core/persistence.hpp>
#include <opencv2/core/version.hpp>
#include <opencv2/core/cuda.hpp>

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
    // 加载图像
    Mat img = imread("lena.jpg", IMREAD_GRAYSCALE);

    // 进行小波变换
    Mat dst;
    pyrDown(img, dst);
    pyrUp(dst, dst, img.size());
    Mat coeffs;
    pyrDown(dst, coeffs);
    pyrUp(coeffs, coeffs, dst.size());
    coeffs = img - coeffs;

    // 对小波系数进行阈值处理，设定阈值为sigma*sqrt(2*log2(n))
    double sigma = 1.5;
    double T = sigma * sqrt(2 * log2(img.size().area()));
    Mat coeffs_thresh = coeffs.clone();
    for (int i = 0; i < coeffs.rows; i++)
    {
        for (int j = 0; j < coeffs.cols; j++)
        {
            if (coeffs.at<uchar>(i, j) < T)
            {
                coeffs_thresh.at<uchar>(i, j) = 0;
            }
        }
    }

    // 进行小波逆变换
    pyrUp(coeffs_thresh, coeffs_thresh, dst.size());
    pyrDown(coeffs_thresh, coeffs_thresh);
    pyrUp(coeffs_thresh, coeffs_thresh, img.size());
    Mat img_denoised = img - coeffs_thresh;

    // 显示原图和去噪后的图像
    imshow("Original", img);
    imshow("Denoised", img_denoised);
    waitKey(0);
    destroyAllWindows();

    return 0;
}

在这个例子中，我们使用了pyrDown和pyrUp函数对图像进行小波分解和逆变换，阈值使用了sigma*sqrt(2*log2(n))的公式进行计算，其中sigma为一个参数，可以根据具体情况进行调整。