多尺度的图像细节提升multiScaleSharpen实现方法
多尺度的图像细节提升multiScaleSharpen实现方法
图像增强:多尺度的图像细节提升(multi-scale detail boosting)实现方法
#include <iostream> #include <opencv2\opencv.hpp> #include <opencv2\highgui\highgui.hpp> using namespace std; using namespace cv; cv::Mat multiScaleSharpen(cv::Mat Src, int Radius) { int rows = Src.rows; int cols = Src.cols; int cha = Src.channels(); cv::Mat B1, B2, B3; GaussianBlur(Src, B1, Size(Radius, Radius), 1.0, 1.0);//高斯模糊 GaussianBlur(Src, B2, Size(Radius*2-1, Radius*2-1), 2.0, 2.0); GaussianBlur(Src, B3, Size(Radius*4-1, Radius*4-1), 4.0, 4.0); double w1 = 0.5; double w2 = 0.5; double w3 = 0.25; cv::Mat dest = cv::Mat::zeros(Src.size(), Src.type()); for (size_t i = 0; i < rows; i++) { uchar* src_ptr = Src.ptr<uchar>(i); uchar* dest_ptr = dest.ptr<uchar>(i); uchar* B1_ptr = B1.ptr<uchar>(i); uchar* B2_ptr = B2.ptr<uchar>(i); uchar* B3_ptr = B3.ptr<uchar>(i); for (size_t j = 0; j < cols; j++) { for (size_t c = 0; c < cha; c++) { int D1 = src_ptr[3*j+c] - B1_ptr[3 * j + c]; int D2 = B1_ptr[3 * j + c] - B2_ptr[3 * j + c]; int D3 = B2_ptr[3 * j + c] - B3_ptr[3 * j + c]; int sign = (D1 > 0) ? 1 : -1; dest_ptr[3 * j + c] = saturate_cast<uchar>((1 - w1*sign)*D1 - w2*D2 + w3*D3 + src_ptr[3 * j + c]); } } } return dest; } int main(int argc) { Mat src = imread("image\\test.jpg"); cv::imshow("src", src); cvWaitKey(100); cv::Mat dest=multiScaleSharpen(src,5); cv::imshow("dest", dest); cvWaitKey(0); return 0; }
作为一种简单可行的方法，是有效果的。并入GOCvHelper库中，并且向OpenCV进行推送。
为了进行pr的资料准备，需要做以下研究：
1、算法原理，比较能够将清楚的地方；(OK继续）
2、此类代码放在哪里比较合适，并且确保编译正确；
3、完善文档，提炼价值。
该算法来自论文
DARK IMAGE ENHANCEMENT BASED ON PAIRWISE TARGET CONTRAST AND MULTI-SCALE DETAIL BOOSTING
的第2.3节
论文的核心思想类似于Retinex，使用了三个尺度的高斯模糊，再和原图做减法，获得不同程度的细节信息，然后通过一定的组合方式把这些细节信息融合到原图中，从而得到加强原图信息的能力。值得一提的就是对D1的系数做了特殊的处理，算法的编码简单、效果明显。
对应的python实现
# -*- coding: utf-8 -*- import cv2 import numpy as np from numpy import uint8 from numpy import float32 from numpy import hstack def multiScaleSharpen(img ,radius): h,w,chan = img.shape GaussBlue1 = np.zeros(img.shape,dtype = uint8) GaussBlue2 = np.zeros(img.shape, dtype=uint8) GaussBlue3 = np.zeros(img.shape, dtype=uint8) Dest_float_img = np.zeros(img.shape, dtype=float32) Dest_img = np.zeros(img.shape, dtype=uint8) w1 = 0.5 w2 = 0.5 w3 = 0.25 GaussBlue1 = cv2.GaussianBlur(img,(radius,radius),1) GaussBlue2 = cv2.GaussianBlur(img,(radius*2-1,radius*2-1),2) GaussBlue3 = cv2.GaussianBlur(img,(radius*4-1,radius*4-1),4) for i in range(0,h): for j in range(0,w): for k in range(0,chan): Src = img.item(i,j,k) D1 = Src-GaussBlue1.item(i,j,k) D2 = GaussBlue1.item(i,j,k) - GaussBlue2.item(i,j,k) D3 = GaussBlue2.item(i,j,k) - GaussBlue3.item(i,j,k) if(D1 > 0): sig = 1 else: sig = -1 Dest_float_img.itemset((i,j,k),(1-w1*sig)*D1+w2*D2+w3*D3+Src) Dest_img = cv2.convertScaleAbs(Dest_float_img) return Dest_img if __name__ == '__main__': img = cv2.imread("175_result.bmp") #img = cv2.imread("128.jpg") multiScaleSharpen_out = np.zeros(img.shape, dtype=uint8) multiScaleSharpen_out = multiScaleSharpen(img,5)#jishu cv2.imwrite("multiScaleSharpen_175_result.bmp", multiScaleSharpen_out)
OpenCV自己是否有Shappen的东西？值得研究。
#include "opencv2/core/utility.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/imgcodecs.hpp" #include "opencv2/highgui.hpp" #include <stdio.h> using namespace cv; using namespace std; int sharpenRadius = 1; Mat image, sharpen; const char* window_name1 = "multiScaleSharpen"; Mat multiScaleSharpen(Mat Src, int Radius) { int rows = Src.rows; int cols = Src.cols; int cha = Src.channels(); Mat B1, B2, B3; GaussianBlur(Src, B1, Size(Radius, Radius), 1.0, 1.0); GaussianBlur(Src, B2, Size(Radius * 2 - 1, Radius * 2 - 1), 2.0, 2.0); GaussianBlur(Src, B3, Size(Radius * 4 - 1, Radius * 4 - 1), 4.0, 4.0); double w1 = 0.5; double w2 = 0.5; double w3 = 0.25; cv::Mat dest = cv::Mat::zeros(Src.size(), Src.type()); for (size_t i = 0; i < rows; i++) { uchar* src_ptr = Src.ptr<uchar>(i); uchar* dest_ptr = dest.ptr<uchar>(i); uchar* B1_ptr = B1.ptr<uchar>(i); uchar* B2_ptr = B2.ptr<uchar>(i); uchar* B3_ptr = B3.ptr<uchar>(i); for (size_t j = 0; j < cols; j++) { for (size_t c = 0; c < cha; c++) { int D1 = src_ptr[3 * j + c] - B1_ptr[3 * j + c]; int D2 = B1_ptr[3 * j + c] - B2_ptr[3 * j + c]; int D3 = B2_ptr[3 * j + c] - B3_ptr[3 * j + c]; int sign = (D1 > 0) ? 1 : -1; dest_ptr[3 * j + c] = saturate_cast<uchar>((1 - w1 * sign)*D1 - w2 * D2 + w3 * D3 + src_ptr[3 * j + c]); } } } return dest; } // define a trackbar callback static void onTrackbar(int, void*) { sharpen = multiScaleSharpen(image, sharpenRadius *2+1); imshow(window_name1, sharpen); } static void help(const char** argv) { printf("\nThis sample demonstrates multiScaleSharpen detection\n" "Call:\n" " %s [image_name -- Default is lena.jpg]\n\n", argv[0]); } const char* keys = { "{help h||}{@image |lena.jpg|input image name}" }; int main( int argc, const char** argv ) { help(argv); CommandLineParser parser(argc, argv, keys); string filename = parser.get<string>(0); image = imread(samples::findFile(filename), IMREAD_COLOR); if(image.empty()) { printf("Cannot read image file: %s\n", filename.c_str()); help(argv); return -1; } // Create a window namedWindow(window_name1, 1); // create a toolbar createTrackbar("Canny threshold default", window_name1, &sharpenRadius, 7, onTrackbar); // Show the image onTrackbar(0, 0); // Wait for a key stroke; the same function arranges events processing waitKey(0); return 0;}
给提供了：
void Sharpen(const Mat& myImage,Mat& Result) { //! [8_bit] CV_Assert(myImage.depth() == CV_8U); // accept only uchar images //! [8_bit] //! [create_channels] const int nChannels = myImage.channels(); Result.create(myImage.size(),myImage.type()); //! [create_channels] //! [basic_method_loop] 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]); } } //! [basic_method_loop] //! [borders] 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)); //! [borders] }
