/*
 *本程式的目的旨在對前面介紹的濾波器進行一個比較
 *對比每種濾波器的濾波效果和耗時
 *除雙邊濾波外其他濾波器kernel均是5*5或5
 *對於雙邊濾波器取其直徑典型值30
 *sigmaColor和sigmaSpace的值均由直徑計算而來
*/

#include <iostream>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>

using namespace std;
using namespace cv;

int
 
 main()
{
    Mat srcImage = imread("lena.jpg");

    if(srcImage.empty())
    {
        cout << "圖片載入失敗!" << endl;
        return -1;
    }
    else
        cout << "圖片載入成功!" << endl << endl;

    namedWindow("原影象", WINDOW_AUTOSIZE);
    imshow("原影象", srcImage);

    /********************方框濾波*******************/
 

    Mat dstImage_boxFilter;
    double time_boxFilter = static_cast<double>(getTickCount());
    boxFilter(srcImage, dstImage_boxFilter, -1, Size(5, 5));
    time_boxFilter = ((double)getTickCount()-time_boxFilter)/getTickFrequency();
    cout << "方框濾波耗時: " << time_boxFilter << " s"
 
 << endl;
    namedWindow("方框濾波影象", WINDOW_AUTOSIZE);
    imshow("方框濾波影象", dstImage_boxFilter);

    /********************均值濾波*****************/
    Mat dstImage_blur;
    double time_blur = static_cast<double>(getTickCount());
    blur(srcImage, dstImage_blur, Size(5, 5));
    time_blur = ((double)getTickCount()-time_blur)/getTickFrequency();
    cout << "均值濾波耗時: " << time_blur << " s" << endl;
    namedWindow("均值濾波影象", WINDOW_AUTOSIZE);
    imshow("均值濾波影象", dstImage_blur);

    /********************高斯濾波******************/
    Mat dstImage_GaussianBlur;
    double time_GaussianBlur = static_cast<double>(getTickCount());
    GaussianBlur(srcImage, dstImage_GaussianBlur, Size(5, 5), 0, 0);
    time_GaussianBlur = ((double)getTickCount()-time_GaussianBlur)/getTickFrequency();
    cout << "高斯濾波耗時: " << time_GaussianBlur << " s" << endl;
    namedWindow("高斯濾波影象", WINDOW_AUTOSIZE);
    imshow("高斯濾波影象", dstImage_GaussianBlur);

    /*******************中值濾波*******************/
    Mat dstImage_medianBlur;
    double time_medianBlur=static_cast<double>(getTickCount());
    medianBlur(srcImage, dstImage_medianBlur, 5);
    time_medianBlur = ((double)getTickCount()-time_medianBlur)/getTickFrequency();
    cout << "中值濾波耗時: " << time_medianBlur << " s" << endl;
    namedWindow("中值濾波影象", WINDOW_AUTOSIZE);
    imshow("中值濾波影象", dstImage_medianBlur);

    /******************雙邊濾波******************/
    Mat dstImage_bilateralFilter;
    double time_bilateralFilter = static_cast<double>(getTickCount());
    bilateralFilter(srcImage, dstImage_bilateralFilter, 30, 60, 15);
    time_bilateralFilter=((double)getTickCount()-time_bilateralFilter)/getTickFrequency();
    cout << "雙邊濾波耗時: " << time_bilateralFilter << " s" << endl;
    namedWindow("雙邊濾波影象", WINDOW_AUTOSIZE);
    imshow("雙邊濾波影象", dstImage_bilateralFilter);

    waitKey(0);

    return 0;
}

程式執行結果：

PS:程式執行時間與編譯環境和電腦硬體都有關係，本結果僅供參考，以自己實驗結果為準！！！