矩陣的掩模操作（通常也叫做卷積操作）非常簡單。本文的中心思想是基于掩模矩陣（也稱為內(nèi)核或者卷積核）重新計算圖像每個像素的值。此掩模矩陣（卷積核）的值定義了當(dāng)前像素和相鄰像素對新像素值進(jìn)行影響的值。From a mathematical point of view we make a weighted average, with our specified values.(從數(shù)學(xué)的角度來看，基于掩模矩陣指定的值進(jìn)行mask操作后再進(jìn)行加權(quán)平均)。具體（實(shí)現(xiàn)銳化效果的）描述如下：

第一個是公式的形式; 第二個是基于掩模矩陣的版本。兩個等價。

I(i,j)=5?I(i,j)?[I(i?1,j)+I(i+1,j)+I(i,j?1)+I(i,j+1)]
等價于

I(i,j)?M,其中M=0?10?15?10?10

實(shí)現(xiàn)銳化效果的例子代碼如下：

#include <opencv2/imgcodecs.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> #include <iostream>using namespace std; using namespace cv;static void help(char* progName) {cout << endl<< "This program shows how to filter images with mask: the write it yourself and the"<< "filter2d way. " << endl<< "Usage:" << endl<< progName << " [image_name -- default lena.jpg] [G -- grayscale] " << endl << endl; }void Sharpen(const Mat& myImage,Mat& Result);int main( int argc, char* argv[]) {help(argv[0]);const char* filename = argc >=2 ? argv[1] : "lena.jpg";Mat src, dst0, dst1;if (argc >= 3 && !strcmp("G", argv[2]))src = imread( filename, IMREAD_GRAYSCALE);elsesrc = imread( filename, IMREAD_COLOR);if (src.empty()){cerr << "Can't open image [" << filename << "]" << endl;return -1;}namedWindow("Input", WINDOW_AUTOSIZE);namedWindow("Output", WINDOW_AUTOSIZE);imshow( "Input", src );double t = (double)getTickCount();Sharpen( src, dst0 );t = ((double)getTickCount() - t)/getTickFrequency();cout << "Hand written function times passed in seconds: " << t << endl;imshow( "Output", dst0 );waitKey();//掩模矩陣定義初始化，用于filter2D函數(shù)Mat kernel = (Mat_<char>(3,3) << 0, -1, 0,-1, 5, -1,0, -1, 0);t = (double)getTickCount();filter2D( src, dst1, src.depth(), kernel );t = ((double)getTickCount() - t)/getTickFrequency();cout << "Built-in filter2D time passed in seconds: " << t << endl;imshow( "Output", dst1 );waitKey();return 0; }//基于公式計算的版本 void Sharpen(const Mat& myImage,Mat& Result)//公式計算方式 {CV_Assert(myImage.depth() == CV_8U); // accept only uchar imagesconst int nChannels = myImage.channels();Result.create(myImage.size(),myImage.type());for(int j = 1 ; j < myImage.rows-1; ++j){const uchar* previous = myImage.ptr<uchar>(j - 1);const uchar* current = myImage.ptr<uchar>(j );const uchar* next = myImage.ptr<uchar>(j + 1);uchar* output = Result.ptr<uchar>(j);for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i){*output++ = saturate_cast<uchar>(5*current[i]-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);}}Result.row(0).setTo(Scalar(0));Result.row(Result.rows-1).setTo(Scalar(0));Result.col(0).setTo(Scalar(0));Result.col(Result.cols-1).setTo(Scalar(0)); }

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