This pullrequest changes 0516

This enhanced algorithm comes from paper
Dark image enhancement based on binocular target contrast and multi-scale detail enhancement
https://ieeexplore.ieee.org/abstract/document/7351031/
Section 2.3，there is Screenshot

The core idea of ​​the paper is similar to Retinex, which uses Gaussian blur at three scales, and then subtracts from the original image to obtain different levels of detail information, and then merges these details into the original image through a certain combination, which is strengthened The ability of original image information.
It is worth mentioning that the coefficients of D1 have been specially processed. The algorithm has simple coding and obvious effects.

Core function：

Mat multiScaleSharpen(Mat Src, int Radius)
{
    int rows = Src.rows;
    int cols = Src.cols;
    int channels = Src.channels();
    Mat B1, B2, B3;
    GaussianBlur(Src, B1, Size(Radius, Radius), 1.0, 1.0);
    GaussianBlur(Src, B2, Size(Radius * 2 - 1, Radius * 2 - 1), 2.0, 2.0);
    GaussianBlur(Src, B3, Size(Radius * 4 - 1, Radius * 4 - 1), 4.0, 4.0);
    double w1 = 0.5;
    double w2 = 0.5;
    double w3 = 0.25;
    cv::Mat dest = cv::Mat::zeros(Src.size(), Src.type());
    for (size_t i = 0; i < rows; i++)
    {
        uchar* src_ptr = Src.ptr<uchar>(i);
        uchar* dest_ptr = dest.ptr<uchar>(i);
        uchar* B1_ptr = B1.ptr<uchar>(i);
        uchar* B2_ptr = B2.ptr<uchar>(i);
        uchar* B3_ptr = B3.ptr<uchar>(i);
        for (size_t j = 0; j < cols; j++)
        {
            for (size_t c = 0; c < channels; c++)
            {
                int  D1 = src_ptr[3 * j + c] - B1_ptr[3 * j + c];
                int  D2 = B1_ptr[3 * j + c] - B2_ptr[3 * j + c];
                int  D3 = B2_ptr[3 * j + c] - B3_ptr[3 * j + c];
                int  sign = (D1 > 0) ? 1 : -1;
                dest_ptr[3 * j + c] = saturate_cast<uchar>((1 - w1 * sign)*D1 - w2 * D2 + w3 * D3 + src_ptr[3 * j + c]);
            }
        }
    }
    return dest;
}

Results show：

CPP Shampe was PR to illustrate the algorithm。

Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under OpenCV (BSD) License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
• The PR is proposed to proper branch
• There is reference to original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake