平台:Windows 10 20H2

Visual Studio 2015
OpenCV 4.5.3

本文所用源码修改自双边滤波(bilateral filter)以及联合双边滤波(joint bilateral filter)—— flow_specter

源码

滤波器

// 双边滤波
// @ src 待滤波的影像
// @ dst 输出的影像
void BilateralFilter(Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace)
{
    dst = src.clone();
    int n_rows = dst.rows;
    int n_cols = dst.cols;
    int n_channels = dst.channels();
    int n_cols_with_channels = n_cols * n_channels;
    int half_kernel_size = d / 2;

    int index;
    double pixel_sum;
    double weight_sum = 0;
    double temp_bilateral_weight = 0;
    double color_kernel[256];

    // 颜色域权重确定
    // @ color_kernel 颜色域核,1D,长度为256
    for (int i = 0; i < 256; i++)
    {
        color_kernel[i] = exp(-1.0 * (i * i) / (2 * sigmaColor * sigmaColor));
    }

    // 空间域权重确定
    // @ distance_kernel 空间域核,1D
    // **************************************************************************************************************
    double *distance_kernel;
    distance_kernel = new double[d * d];

    int k = d / 2;

    //二维动态数组申请空间
    double **distance_kernel_2D = new double*[d];
    for (int i = 0; i < d; i++)
        distance_kernel_2D[i] = new double[d];

    double delta_square = 2 * sigmaSpace * sigmaSpace; //分母
    for (int i = -k; i <= k; i++)
    {
        for (int j = -k; j <= k; j++)
        {
            double distance_numerator = i * i + j * j;
            distance_kernel_2D[i + k][j + k] = exp(-1.0 * distance_numerator / delta_square);
        }
    }
    // 将2D kernel 转换为 1D kernel
    for (int i = 0; i < d; i++)
    {
        for (int j = 0; j < d; j++)
        {
            distance_kernel[d * i + j] = distance_kernel_2D[i][j];
        }
    }

    //释放二维动态数组空间
    for (int i = 0; i < d; i++)
        delete[] distance_kernel_2D[i];
    delete[] distance_kernel_2D;
    // **************************************************************************************************************

    // 边界不做处理
    for (int i = half_kernel_size; i < (n_rows - half_kernel_size); i++) 
    {
        uchar* pt_dst = dst.ptr<uchar>(i);
        uchar* pt_src = src.ptr<uchar>(i);
        for (int j = n_channels * half_kernel_size; j < (n_cols_with_channels - n_channels * half_kernel_size); j++) 
        {
            index = 0;
            pixel_sum = weight_sum = 0;

            // 内层kx,ky循环,空间域内滤波
            for (int kx = i - half_kernel_size; kx <= i + half_kernel_size; kx++) 
            {
                uchar* pt_k_src = src.ptr<uchar>(kx);
                for (int ky = j - n_channels * half_kernel_size; ky <= (j + n_channels * half_kernel_size); ky += n_channels) 
                {
                    temp_bilateral_weight = distance_kernel[index++] * color_kernel[(int)abs(pt_src[j] - pt_k_src[ky])];
                    weight_sum += temp_bilateral_weight;
                    pixel_sum += (pt_k_src[ky] * temp_bilateral_weight); // 邻域某像素与中心点的双边权重乘积
                }
            }

            pixel_sum /= weight_sum; // 归一化
            pt_dst[j] = saturate_cast<uchar>(pixel_sum); //加权赋值
        }
    }
    delete[]distance_kernel;
}

主函数

图片路径根据实际情况调整,注意反斜杠是转义字符的开头,故“\”应替换为“\”

int main(int argc, char * argv[])
{
    Mat src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\face.jpg");
    Mat dst;

    BilateralFilter(src, dst, 23, 35, 10);

    imshow("原图", src);
    imshow("输出", dst);

    waitKey(0);

    return 0;
}

效果

在这里插入图片描述

完整源码

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

using namespace cv;
using namespace std;

// 双边滤波
// @ src 待滤波的影像
// @ dst 输出的影像
void BilateralFilter(Mat& src, Mat& dst, int d, double sigmaColor, double sigmaSpace)
{
    dst = src.clone();
    int n_rows = dst.rows;
    int n_cols = dst.cols;
    int n_channels = dst.channels();
    int n_cols_with_channels = n_cols * n_channels;
    int half_kernel_size = d / 2;

    int index;
    double pixel_sum;
    double weight_sum = 0;
    double temp_bilateral_weight = 0;
    double color_kernel[256];

    // 颜色域权重确定
    // @ color_kernel 颜色域核,1D,长度为256
    for (int i = 0; i < 256; i++)
    {
        color_kernel[i] = exp(-1.0 * (i * i) / (2 * sigmaColor * sigmaColor));
    }

    // 空间域权重确定
    // @ distance_kernel 空间域核,1D
    // **************************************************************************************************************
    double *distance_kernel;
    distance_kernel = new double[d * d];

    int k = d / 2;

    //二维动态数组申请空间
    double **distance_kernel_2D = new double*[d];
    for (int i = 0; i < d; i++)
        distance_kernel_2D[i] = new double[d];

    double delta_square = 2 * sigmaSpace * sigmaSpace; //分母
    for (int i = -k; i <= k; i++)
    {
        for (int j = -k; j <= k; j++)
        {
            double distance_numerator = i * i + j * j;
            distance_kernel_2D[i + k][j + k] = exp(-1.0 * distance_numerator / delta_square);
        }
    }
    // 将2D kernel 转换为 1D kernel
    for (int i = 0; i < d; i++)
    {
        for (int j = 0; j < d; j++)
        {
            distance_kernel[d * i + j] = distance_kernel_2D[i][j];
        }
    }

    //释放二维动态数组空间
    for (int i = 0; i < d; i++)
        delete[] distance_kernel_2D[i];
    delete[] distance_kernel_2D;
    // **************************************************************************************************************

    // 边界不做处理
    for (int i = half_kernel_size; i < (n_rows - half_kernel_size); i++) 
    {
        uchar* pt_dst = dst.ptr<uchar>(i);
        uchar* pt_src = src.ptr<uchar>(i);
        for (int j = n_channels * half_kernel_size; j < (n_cols_with_channels - n_channels * half_kernel_size); j++) 
        {
            index = 0;
            pixel_sum = weight_sum = 0;

            // 内层kx,ky循环,空间域内滤波
            for (int kx = i - half_kernel_size; kx <= i + half_kernel_size; kx++) 
            {
                uchar* pt_k_src = src.ptr<uchar>(kx);
                for (int ky = j - n_channels * half_kernel_size; ky <= (j + n_channels * half_kernel_size); ky += n_channels) 
                {
                    temp_bilateral_weight = distance_kernel[index++] * color_kernel[(int)abs(pt_src[j] - pt_k_src[ky])];
                    weight_sum += temp_bilateral_weight;
                    pixel_sum += (pt_k_src[ky] * temp_bilateral_weight); // 邻域某像素与中心点的双边权重乘积
                }
            }

            pixel_sum /= weight_sum; // 归一化
            pt_dst[j] = saturate_cast<uchar>(pixel_sum); //加权赋值
        }
    }
    delete[]distance_kernel;
}

int main(int argc, char * argv[])
{
    Mat src = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\face.jpg");
    Mat dst;

    BilateralFilter(src, dst, 23, 35, 10);

    imshow("原图", src);
    imshow("输出", dst);

    waitKey(0);

    return 0;
}