空域滤波

Content

  • 利用均值模板平滑灰度图像

    具体内容:利用OpenCV对图像像素进行操作，分别利用3*3、5*5和9*9尺寸的均值模板平滑灰度图像。

  • 利用高斯模板平滑灰度图像

    具体内容: 利用OpenCV对图像像素进行操作，分别利用3*3、5*5和9*9尺寸的高斯模板平滑灰度图像。

  • 利用Laplacian、Robert、Sobel模板锐化灰度图像

    具体内容: 利用OpenCV对图像像素进行操作，分别利用Laplacian、Robert、Sobel模板锐化灰度图像。

  • 利用高提升滤波算法增强灰度图像

    具体内容:利用OpenCV对图像像素进行操作，设计高提升滤波算法增强图像。

  • 利用均值模板平滑彩色图像

    具体内容: 利用OpenCV分别对图像像素的RGB三个通道进行操作，分别利用3*3、5*5和9*9尺寸的均值模板平滑彩色图像。

  • 利用高斯模板平滑彩色图像

    具体内容: 利用OpenCV分别对图像像素的RGB三个通道进行操作，分别利用3*3、5*5和9*9尺寸的高斯模板平滑彩色图像。

  • 利用Laplacian、Robert、Sobel模板锐化彩色图像

    具体内容: 利用OpenCV分别对图像像素的RGB三个通道进行操作，分别利用Laplacian、Robert、Sobel模板锐化彩色图像。

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#include <iostream>
#include <vector>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;

// 灰度图像均值滤波
Mat meanFilter_Gray(const Mat& srcImage, int ksize)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int nHalfSize = ksize / 2;
	for (int i = nHalfSize; i < nRows - nHalfSize; i++)
	{
		for (int j = nHalfSize; j < nCols - nHalfSize; j++)
		{
			int sum = 0;
			for (int m = -nHalfSize; m <= nHalfSize; m++)
			{
				for (int n = -nHalfSize; n <= nHalfSize; n++)
				{
					sum += srcImage.at<uchar>(i + m, j + n);
				}
			}
			dstImage.at<uchar>(i, j) = sum / (ksize * ksize);
		}
	}
	return dstImage;
}

// 彩色图像均值滤波
Mat meanFilter_RGB(const Mat& srcImage, int ksize)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int nHalfSize = ksize / 2;
	for (int i = nHalfSize; i < nRows - nHalfSize; i++)
	{
		for (int j = nHalfSize; j < nCols - nHalfSize; j++)
		{
			int sumB = 0;
			int sumG = 0;
			int sumR = 0;
			for (int m = -nHalfSize; m <= nHalfSize; m++)
			{
				for (int n = -nHalfSize; n <= nHalfSize; n++)
				{
					sumB += srcImage.at<Vec3b>(i + m, j + n)[0];
					sumG += srcImage.at<Vec3b>(i + m, j + n)[1];
					sumR += srcImage.at<Vec3b>(i + m, j + n)[2];
				}
			}
			dstImage.at<Vec3b>(i, j)[0] = sumB / (ksize * ksize);
			dstImage.at<Vec3b>(i, j)[1] = sumG / (ksize * ksize);
			dstImage.at<Vec3b>(i, j)[2] = sumR / (ksize * ksize);
		}
	}
	return dstImage;
}

// 灰度图像高斯滤波
Mat gaussianFilter_Gray(const Mat& srcImage, int ksize, double sigma)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int nHalfSize = ksize / 2;
	double* kernel = new double[ksize * ksize];
	double sum = 0;
	for (int i = -nHalfSize; i <= nHalfSize; i++)
	{
		for (int j = -nHalfSize; j <= nHalfSize; j++)
		{
			kernel[(i + nHalfSize) * ksize + j + nHalfSize] = exp(-(i * i + j * j) / (2 * sigma * sigma));
			sum += kernel[(i + nHalfSize) * ksize + j + nHalfSize];
		}
	}
	for (int i = 0; i < ksize * ksize; i++)
	{
		kernel[i] /= sum;
	}
	for (int i = nHalfSize; i < nRows - nHalfSize; i++)
	{
		for (int j = nHalfSize; j < nCols - nHalfSize; j++)
		{
			double sum = 0;
			for (int m = -nHalfSize; m <= nHalfSize; m++)
			{
				for (int n = -nHalfSize; n <= nHalfSize; n++)
				{
					sum += srcImage.at<uchar>(i + m, j + n) * kernel[(m + nHalfSize) * ksize + n + nHalfSize];
				}
			}
			dstImage.at<uchar>(i, j) = sum;
		}
	}
	return dstImage;
}

// 彩色图像高斯滤波
Mat gaussianFilter_RGB(const Mat& srcImage, int ksize, double sigma)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int nHalfSize = ksize / 2;
	double* kernel = new double[ksize * ksize];
	double sum = 0;
	for (int i = -nHalfSize; i <= nHalfSize; i++)
	{
		for (int j = -nHalfSize; j <= nHalfSize; j++)
		{
			kernel[(i + nHalfSize) * ksize + j + nHalfSize] = exp(-(i * i + j * j) / (2 * sigma * sigma));
			sum += kernel[(i + nHalfSize) * ksize + j + nHalfSize];
		}
	}
	for (int i = 0; i < ksize * ksize; i++)
	{
		kernel[i] /= sum;
	}
	for (int i = nHalfSize; i < nRows - nHalfSize; i++)
	{
		for (int j = nHalfSize; j < nCols - nHalfSize; j++)
		{
			double sumB = 0;
			double sumG = 0;
			double sumR = 0;
			for (int m = -nHalfSize; m <= nHalfSize; m++)
			{
				for (int n = -nHalfSize; n <= nHalfSize; n++)
				{
					sumB += srcImage.at<Vec3b>(i + m, j + n)[0] * kernel[(m + nHalfSize) * ksize + n + nHalfSize];
					sumG += srcImage.at<Vec3b>(i + m, j + n)[1] * kernel[(m + nHalfSize) * ksize + n + nHalfSize];
					sumR += srcImage.at<Vec3b>(i + m, j + n)[2] * kernel[(m + nHalfSize) * ksize + n + nHalfSize];
				}
			}
			dstImage.at<Vec3b>(i, j)[0] = sumB;
			dstImage.at<Vec3b>(i, j)[1] = sumG;
			dstImage.at<Vec3b>(i, j)[2] = sumR;
		}
	}
	return dstImage;
}

// 灰度图拉普拉斯滤波
Mat laplacianFilter_Gray(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernel[3][3] = { {0, 1, 0}, {1, -4, 1}, {0, 1, 0} };
	for (int i = 1; i < nRows - 1; i++)
	{
		for (int j = 1; j < nCols - 1; j++)
		{
			int sum = 0;
			for (int m = -1; m <= 1; m++)
			{
				for (int n = -1; n <= 1; n++)
				{
					sum += srcImage.at<uchar>(i + m, j + n) * kernel[m + 1][n + 1];
				}
			}
			dstImage.at<uchar>(i, j) = sum;
		}
	}
	return dstImage;
}

// 彩色图像拉普拉斯滤波
Mat laplacianFilter_RGB(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernel[3][3] = { {0, 1, 0}, {1, -4, 1}, {0, 1, 0} };
	for (int i = 1; i < nRows - 1; i++)
	{
		for (int j = 1; j < nCols - 1; j++)
		{
			int sumB = 0;
			int sumG = 0;
			int sumR = 0;
			for (int m = -1; m <= 1; m++)
			{
				for (int n = -1; n <= 1; n++)
				{
					sumB += srcImage.at<Vec3b>(i + m, j + n)[0] * kernel[m + 1][n + 1];
					sumG += srcImage.at<Vec3b>(i + m, j + n)[1] * kernel[m + 1][n + 1];
					sumR += srcImage.at<Vec3b>(i + m, j + n)[2] * kernel[m + 1][n + 1];
				}
			}
			dstImage.at<Vec3b>(i, j)[0] = sumB;
			dstImage.at<Vec3b>(i, j)[1] = sumG;
			dstImage.at<Vec3b>(i, j)[2] = sumR;
		}
	}
	return dstImage;
}

// 灰度图Roberts滤波
Mat robertFilter_Gray(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernelX[2][2] = { {-1, 0}, {0, 1} };
	int kernelY[2][2] = { {0, -1}, {1, 0} };
	for (int i = 0; i < nRows - 1; i++)
	{
		for (int j = 0; j < nCols - 1; j++)
		{
			int sumX = 0;
			int sumY = 0;
			for (int m = 0; m <= 1; m++)
			{
				for (int n = 0; n <= 1; n++)
				{
					sumX += srcImage.at<uchar>(i + m, j + n) * kernelX[m][n];
					sumY += srcImage.at<uchar>(i + m, j + n) * kernelY[m][n];
				}
			}
			dstImage.at<uchar>(i, j) = abs(sumX) + abs(sumY);
		}
	}
	return dstImage;
}

// 彩色图Roberts滤波
Mat robertFilter_RGB(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernelX[2][2] = { {-1, 0}, {0, 1} };
	int kernelY[2][2] = { {0, -1}, {1, 0} };
	for (int i = 0; i < nRows - 1; i++)
	{
		for (int j = 0; j < nCols - 1; j++)
		{
			int sumX_B = 0;
			int sumY_B = 0;
			int sumX_G = 0;
			int sumY_G = 0;
			int sumX_R = 0;
			int sumY_R = 0;
			for (int m = 0; m <= 1; m++)
			{
				for (int n = 0; n <= 1; n++)
				{
					sumX_B += srcImage.at<Vec3b>(i + m, j + n)[0] * kernelX[m][n];
					sumY_B += srcImage.at<Vec3b>(i + m, j + n)[0] * kernelY[m][n];
					sumX_G += srcImage.at<Vec3b>(i + m, j + n)[1] * kernelX[m][n];
					sumY_G += srcImage.at<Vec3b>(i + m, j + n)[1] * kernelY[m][n];
					sumX_R += srcImage.at<Vec3b>(i + m, j + n)[2] * kernelX[m][n];
					sumY_R += srcImage.at<Vec3b>(i + m, j + n)[2] * kernelY[m][n];
				}
			}
			dstImage.at<Vec3b>(i, j)[0] = abs(sumX_B) + abs(sumY_B);
			dstImage.at<Vec3b>(i, j)[1] = abs(sumX_G) + abs(sumY_G);
			dstImage.at<Vec3b>(i, j)[2] = abs(sumX_R) + abs(sumY_R);
		}
	}
	return dstImage;
}

// 灰度图Sobel滤波
Mat sobelFilter_Gray(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernelX[3][3] = { {1, 0, -1}, {2, 0, -2}, {1, 0, -1} };
	int kernelY[3][3] = { {1, 2, 1}, {0, 0, 0}, {-1, -2, -1} };
	for (int i = 1; i < nRows - 1; i++)
	{
		for (int j = 1; j < nCols - 1; j++)
		{
			int sumX = 0;
			int sumY = 0;
			for (int m = -1; m <= 1; m++)
			{
				for (int n = -1; n <= 1; n++)
				{
					sumX += srcImage.at<uchar>(i + m, j + n) * kernelX[m + 1][n + 1];
					sumY += srcImage.at<uchar>(i + m, j + n) * kernelY[m + 1][n + 1];
				}
			}
			dstImage.at<uchar>(i, j) = sqrt(sumX * sumX + sumY * sumY);
		}
	}
	return dstImage;
}

// 彩色图Sobel滤波
Mat sobelFilter_RGB(const Mat& srcImage)
{
	Mat dstImage = srcImage.clone();
	int nRows = srcImage.rows;
	int nCols = srcImage.cols;
	int kernelX[3][3] = { {1, 0, -1}, {2, 0, -2}, {1, 0, -1} };
	int kernelY[3][3] = { {1, 2, 1}, {0, 0, 0}, {-1, -2, -1} };
	for (int i = 1; i < nRows - 1; i++)
	{
		for (int j = 1; j < nCols - 1; j++)
		{
			int sumX_B = 0;
			int sumY_B = 0;
			int sumX_G = 0;
			int sumY_G = 0;
			int sumX_R = 0;
			int sumY_R = 0;
			for (int m = -1; m <= 1; m++)
			{
				for (int n = -1; n <= 1; n++)
				{
					sumX_B += srcImage.at<Vec3b>(i + m, j + n)[0] * kernelX[m + 1][n + 1];
					sumY_B += srcImage.at<Vec3b>(i + m, j + n)[0] * kernelY[m + 1][n + 1];
					sumX_G += srcImage.at<Vec3b>(i + m, j + n)[1] * kernelX[m + 1][n + 1];
					sumY_G += srcImage.at<Vec3b>(i + m, j + n)[1] * kernelY[m + 1][n + 1];
					sumX_R += srcImage.at<Vec3b>(i + m, j + n)[2] * kernelX[m + 1][n + 1];
					sumY_R += srcImage.at<Vec3b>(i + m, j + n)[2] * kernelY[m + 1][n + 1];
				}
			}
			dstImage.at<Vec3b>(i, j)[0] = abs(sumX_B) + abs(sumY_B);
			dstImage.at<Vec3b>(i, j)[1] = abs(sumX_G) + abs(sumY_G);
			dstImage.at<Vec3b>(i, j)[2] = abs(sumX_R) + abs(sumY_R);
		}
	}
	return dstImage;
}

Mat highBoostFilter_Gray(const Mat& srcImage, double k)
{
	Mat dstImage = gaussianFilter_Gray(srcImage, 7, 1);
	dstImage = srcImage + k * (srcImage - dstImage);
	return dstImage;
}

Mat highBoostFilter_RGB(const Mat& srcImage, double k)
{
	Mat dstImage = gaussianFilter_RGB(srcImage, 7, 1);
	dstImage = srcImage + k * (srcImage - dstImage);
	return dstImage;
}

int main()
{
    
	Mat myImage = imread("wallhaven.jpg");
	//imshow("Image", myImage);
	Mat grayImage;
	// 转换为灰度图像
	cvtColor(myImage, grayImage, COLOR_BGR2GRAY);
	imshow("GrayImage", grayImage);
	imwrite("GrayImage.jpg", grayImage);

	// 灰度图均值滤波 3 5 9
	Mat grayImgAfterMeanFilter3 = meanFilter_Gray(grayImage, 3);
	imshow("GrayImgAfterMeanFilter3", grayImgAfterMeanFilter3);
	imwrite("GrayImgAfterMeanFilter3.jpg", grayImgAfterMeanFilter3);
	Mat grayImgAfterMeanFilter5 = meanFilter_Gray(grayImage, 5);
	imshow("GrayImgAfterMeanFilter5", grayImgAfterMeanFilter5);
	imwrite("GrayImgAfterMeanFilter5.jpg", grayImgAfterMeanFilter5);
	Mat grayImgAfterMeanFilter9 = meanFilter_Gray(grayImage, 9);
	imshow("GrayImgAfterMeanFilter9", grayImgAfterMeanFilter9);
	imwrite("GrayImgAfterMeanFilter9.jpg", grayImgAfterMeanFilter9);

	// 彩色图均值滤波 3 5 9
	Mat rgbImgAfterMeanFilter3 = meanFilter_RGB(myImage, 3);
	imshow("RgbImgAfterMeanFilter3", rgbImgAfterMeanFilter3);
	imwrite("RgbImgAfterMeanFilter3.jpg", rgbImgAfterMeanFilter3);
	Mat rgbImgAfterMeanFilter5 = meanFilter_RGB(myImage, 5);
	imshow("RgbImgAfterMeanFilter5", rgbImgAfterMeanFilter5);
	imwrite("RgbImgAfterMeanFilter5.jpg", rgbImgAfterMeanFilter5);
	Mat rgbImgAfterMeanFilter9 = meanFilter_RGB(myImage, 9);
	imshow("RgbImgAfterMeanFilter9", rgbImgAfterMeanFilter9);
	imwrite("RgbImgAfterMeanFilter9.jpg", rgbImgAfterMeanFilter9);

	// OpenCV自带的均值滤波 用于对比
	//Mat imgAfterMeanFilter_OpenCV;
	//blur(myImage, imgAfterMeanFilter_OpenCV, Size(9, 9));
	//imshow("imgAfterMeanFilter_OpenCV", imgAfterMeanFilter_OpenCV);
	
	// 灰度图高斯滤波 3 5 9
	Mat grayImgAfterGaussianFilter3 = gaussianFilter_Gray(grayImage, 3, 1);
	imshow("GrayImgAfterGaussianFilter3", grayImgAfterGaussianFilter3);
	imwrite("GrayImgAfterGaussianFilter3.jpg", grayImgAfterGaussianFilter3);
	Mat grayImgAfterGaussianFilter5 = gaussianFilter_Gray(grayImage, 5, 1);
	imshow("GrayImgAfterGaussianFilter5", grayImgAfterGaussianFilter5);
	imwrite("GrayImgAfterGaussianFilter5.jpg", grayImgAfterGaussianFilter5);
	Mat grayImgAfterGaussianFilter9 = gaussianFilter_Gray(grayImage, 9, 1);
	imshow("GrayImgAfterGaussianFilter9", grayImgAfterGaussianFilter9);
	imwrite("GrayImgAfterGaussianFilter9.jpg", grayImage);
	
	// 彩色图高斯滤波 3 5 9
	Mat rgbImgAfterGaussianFilter3 = gaussianFilter_RGB(myImage, 3, 1);
	imshow("RgbImgAfterGaussianFilter3", rgbImgAfterGaussianFilter3);
	imwrite("RgbImgAfterGaussianFilter3.jpg", rgbImgAfterGaussianFilter3);
	Mat rgbImgAfterGaussianFilter5 = gaussianFilter_RGB(myImage, 5, 1);
	imshow("RgbImgAfterGaussianFilter5", rgbImgAfterGaussianFilter5);
	imwrite("RgbImgAfterGaussianFilter5.jpg", rgbImgAfterGaussianFilter5);
	Mat rgbImgAfterGaussianFilter9 = gaussianFilter_RGB(myImage, 9, 1);
	imshow("RgbImgAfterGaussianFilter9", rgbImgAfterGaussianFilter9);
	imwrite("RgbImgAfterGaussianFilter9.jpg", rgbImgAfterGaussianFilter9);

	// OpenCV自带的高斯滤波 用于对比
	//Mat imgAfterGaussianFilter_OpenCV;
	//GaussianBlur(myImage, imgAfterGaussianFilter_OpenCV, Size(7, 7), 1);
	//imshow("GrayImgAfterGaussianFilter_OpenCV", imgAfterGaussianFilter_OpenCV);
	
	// 灰度图拉普拉斯滤波
	Mat grayImgAfterLaplacianFilter = laplacianFilter_Gray(grayImage);
	Mat grayImgAfterLaplacianFilterDst=grayImage-grayImgAfterLaplacianFilter;
	imshow("GrayLaplacianDst", grayImgAfterLaplacianFilterDst);
	imwrite("GrayLaplacianDst.jpg", grayImgAfterLaplacianFilterDst);
	imshow("GrayImgAfterLaplacianFilter", grayImgAfterLaplacianFilter);
	imwrite("GrayImgAfterLaplacianFilter.jpg", grayImgAfterLaplacianFilter);

	// 彩色图拉普拉斯滤波
	Mat rgbImgAfterLaplacianFilter = laplacianFilter_RGB(myImage);
	Mat rgbImgAfterLaplacianFilterDst =myImage-rgbImgAfterLaplacianFilter;
	imshow("RGBLaplacianDst", rgbImgAfterLaplacianFilterDst);
	imwrite("RGBLaplacianDst.jpg", rgbImgAfterLaplacianFilterDst);
	imshow("RgbImgAfterLaplacianFilter", rgbImgAfterLaplacianFilter);
	imwrite("RgbImgAfterLaplacianFilter.jpg", rgbImgAfterLaplacianFilter);
	
	// 灰度图Roberts滤波
	Mat grayImgAfterRobertFilter = robertFilter_Gray(grayImage);
	Mat grayImgAfterRobertFilterDst=grayImage-grayImgAfterRobertFilter;
	imshow("GrayRobertDst", grayImgAfterRobertFilterDst);
	imwrite("GrayRobertDst.jpg", grayImgAfterRobertFilterDst);
	imshow("GrayImgAfterRobertFilter", grayImgAfterRobertFilter);
	imwrite("GrayImgAfterRobertFilter.jpg", grayImgAfterRobertFilter);
	 
	// 彩色图Roberts滤波
	Mat rgbImgAfterRobertFilter = robertFilter_RGB(myImage);
	Mat rgbImgAfterRobertFilterDst=myImage-rgbImgAfterRobertFilter;
	imshow("RGBImgRobertDst", rgbImgAfterRobertFilterDst);
	imwrite("RGBImgRobertDst.jpg", rgbImgAfterRobertFilterDst);
	imshow("RgbImgAfterRobertFilter", rgbImgAfterRobertFilter);
	imwrite("RgbImgAfterRobertFilter.jpg", rgbImgAfterRobertFilter);

	// 灰度图Sobel滤波
	Mat grayImgAfterSobelFilter = sobelFilter_Gray(grayImage);
	Mat grayImgAfterSobelFilterDst=grayImage-grayImgAfterSobelFilter;
	imshow("GraySobelDst", grayImgAfterSobelFilterDst);
	imwrite("GraySobelDst.jpg", grayImgAfterSobelFilterDst);
	imshow("GrayImgAfterSobelFilter", grayImgAfterSobelFilter);
	imwrite("GrayImgAfterSobelFilter.jpg", grayImgAfterSobelFilter);
	
	// 彩色图Sobel滤波
	Mat rgbImgAfterSobelFilter = sobelFilter_RGB(myImage);
	Mat rgbImgAfterSobelFilterDst=myImage-rgbImgAfterSobelFilter;
	imshow("RGBImgSobelDst", rgbImgAfterSobelFilterDst);
	imwrite("RGBImgSobelDst.jpg", rgbImgAfterSobelFilterDst);
	imshow("RgbImgAfterSobelFilter", rgbImgAfterSobelFilter);
	imwrite("RgbImgAfterSobelFilter.jpg", rgbImgAfterSobelFilter);

	// 灰度图高提升滤波
	Mat grayImgAfterHighBoostFilter = highBoostFilter_Gray(grayImage, 1.5);
	imshow("GrayImgAfterHighBoostFilter", grayImgAfterHighBoostFilter);
	imwrite("GrayImgAfterHighBoostFilter.jpg", grayImgAfterHighBoostFilter);

	// 彩色图高提升滤波
	Mat rgbImgAfterHighBoostFilter = highBoostFilter_RGB(myImage, 1.5);
	imshow("RgbImgAfterHighBoostFilter", rgbImgAfterHighBoostFilter);
	imwrite("RgbImgAfterHighBoostFilter.jpg", rgbImgAfterHighBoostFilter);

	waitKey(0);
	return 0;
}

Output

灰图 彩图
GrayImage wallhaven
均值模板 3*3 均值模板 3*3
GrayImgAfterMeanFilter3 RgbImgAfterMeanFilter3
均值模板 5*5 均值模板 5*5
GrayImgAfterMeanFilter5 RgbImgAfterMeanFilter5
均值模板 9*9 均值模板 9*9
GrayImgAfterMeanFilter9 RgbImgAfterMeanFilter9
高斯模板 3*3 高斯模板 3*3
GrayImgAfterGaussianFilter3 RgbImgAfterGaussianFilter3
高斯模板 5*5 *高斯模板 55 **
GrayImgAfterGaussianFilter5 RgbImgAfterGaussianFilter5
高斯模板 9*9 高斯模板 9*9
GrayImgAfterGaussianFilter9 RgbImgAfterGaussianFilter9
Laplacian Laplacian
GrayLaplacianDst RGBLaplacianDst
Robert Robert
GrayRobertDst RGBImgRobertDst
Sobel Sobel
GraySobelDst RGBImgSobelDst
高提升滤波 高提升滤波
GrayImgAfterHighBoostFilter RgbImgAfterHighBoostFilter