•利用3*3的Laplacian算子图象锐化：
• Laplace函数实现的方法是先用Sobel 算子计算二阶x和y导数，再求和：
其基本函数模型如下：
Mat getLaplacian(Mat &Image)
{
Mat Scr_Gray,showImage;
int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;
// 使用高斯滤波消除噪声
GaussianBlur(Image, Image, Size(3, 3), 0, 0, BORDER_DEFAULT);
// 转换为灰度图
cvtColor(Image, Scr_Gray, CV_RGB2GRAY);
// 使用Laplace函数
Mat abs_dst;
Laplacian(Scr_Gray, showImage, ddepth, kernel_size, scale, delta, BORDER_DEFAULT);
convertScaleAbs(showImage, abs_dst);
return abs_dst;
}
int main()
{
Mat srcImage_2 = imread("g14.tif");
namedWindow("【原始图均鞭】", 1);
imshow("【原始图均鞭】", srcImage_2);
/*********************对图像进行Laplacian算子***************************/
Mat showImage;
showImage = getLaplacian(srcImage_2);
imshow("Laplacian【效果图均鞭均鞭均鞭】", showImage);
}
最后的应用例程如下所示：
int main()
{
Mat srcImage = imread("1.jpg");
Mat srcImage_1 = imread("g11.tif");
Mat srcImage_2 = imread("g14.tif");
if (!srcImage.data)
{
cout << "fail to load the image" << endl;
return -1;
}
if (!srcImage_1.data)
{
cout << "fail to load the image_1" << endl;
return -2;
}
if (!srcImage_2.data)
{
cout << "fail to load the image_2" << endl;
return -3;
}
//namedWindow("【原始图】", 1);
//imshow("【原始图】", srcImage);
/****************对图像加椒盐噪声，并进行中值滤波******************/
//salt_noise(srcImage, 4000);
//pepper_noise(srcImage, 4000);
//imshow("【噪声图】", srcImage);
//Mat Medical_showImage, Medical_showImage_1;
//MedianFlitering(srcImage, Medical_showImage);
//medianBlur(srcImage, Medical_showImage_1, 3);
//imshow("自定义中值滤波处理后", Medical_showImage);
//imshow("openCV自带的中值滤波", Medical_showImage_1);
/*******************************************************************/
/*********************对图像添加高斯噪声并进行高斯滤波**************/
//Mat GaussianshowImage,GaussianshowImage_1;
//GaussianshowImage_1 = addGaussianNoise(srcImage);
//imshow("高斯噪声【效果图】", GaussianshowImage_1);
//GaussianBlur(GaussianshowImage_1, GaussianshowImage, Size(3, 3), 1);
//imshow("高斯滤波【效果图】", GaussianshowImage);
/*******************************************************************/
/*********************对图像进行椒盐化并进行均值滤波****************/
//Mat image1(srcImage.size(), srcImage.type());
//Mat image2;
//salt_noise(srcImage, 4000);
//pepper_noise(srcImage, 4000);
//imshow("椒盐图【效果图】", srcImage);
//AverFiltering(srcImage, image1);
//blur(srcImage, image2, Size(3, 3));//openCV库自带的均值滤波函数
//imshow("自定义均值滤波", image1);
//imshow("openCV自带的均值滤波", image2);
/*******************************************************************/
/*********************对图像进行Sobel算子***************************/
//Mat showImage, showImage_1;
//showImage=getSobel(srcImage_2);
//imshow("Sobel算子【效果图】", showImage);
/*******************************************************************/
/*********************对图像进行Scharr算子***************************/
//Mat showImage;
//showImage = getScharr(srcImage_2);
//imshow("高通滤波【效果图】", showImage);
/*******************************************************************/
/*********************对图像进行Laplacian算子***************************/
/* Mat showImage;
showImage = getLaplacian(srcImage_2);
imshow("Laplacian【效果图】", showImage);*/
/*******************************************************************/
//Mat showImage = getHistogramImage(srcImage); //得到相应图片的直方图 小狮子
//Mat showImage = getHistogram_Equalization(srcImage);//得到相应图片的直方图的均衡图 小狮子
//imshow("【直方图】", showImage);
//getHistogram_Stetch(srcImage); //得到直方图拉伸之后的图像
/**********************测试代码*****************/
//Mat element = getStructuringElement(MORPH_RECT,Size(15,15));
//Mat dstImage;
//erode(srcImage, dstImage, element);
//imshow("腐蚀操作【效果图】", dstImage);
//blur(srcImage, dstImage,Size(7,7));
//imshow("均值滤波【效果图】", dstImage);
//Mat edge, grayImage;
//cvtColor(srcImage, grayImage, CV_BGR2GRAY);
//blur(grayImage, edge, Size(3, 3));
//Canny(edge, edge, 3, 9, 3);
//imshow("边缘检测【效果图】", edge);
/**********************************************/
waitKey(0);
return 0;
}
部分函数被注释了，想使用的话去掉注释即可
