OpenCV图像增强算子

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我是靠谱客的博主想人陪鞋垫，最近开发中收集的这篇文章主要介绍OpenCV图像增强算子，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

http://www.ihalcon.com/read-12670.html

#include
#include  //头文件
using namespace cv; //包含cv命名空间
using namespace std;
int main()
{

//读入图片
Mat img_1 = imread("F:\图标\222222.jpg");
//Mat img_2 = imread("F:\图标\2.jpg");
if (img_1.empty())
{
cout << "打开图片失败,请检查" << endl;
return -1;
}
imshow("src_img", img_1);

//图像增强算法  直方图均衡化-------------------------------------------------------------------1
Mat imageRGB[3];
split(img_1, imageRGB);
for (int i = 0; i < 3; i++)
{
equalizeHist(imageRGB, imageRGB);
}
merge(imageRGB, 3, img_1);
imshow("直方图均衡化图像增强效果", img_1);

//拉普拉斯算子可以增强局部的图像对比度---------------------------------------------------------2
Mat imageEnhance;
Mat kernel = (Mat_(3, 3) << 0, -1, 0, 0, 5, 0, 0, -1, 0);
filter2D(img_1, imageEnhance, CV_8UC3, kernel);//利用内核实现对图像的卷积运算
imshow("拉普拉斯算子图像增强效果", imageEnhance);

//基于对数Log变换的图像增强,达到了扩展和增强低灰度部分，压缩高灰度部分的值的功能-------------3
Mat imageLog(img_1.size(), CV_32FC3);
for (int i = 0; i < img_1.rows; i++)
{
for (int j = 0; j < img_1.cols; j++)
{
imageLog.at(i, j)[0] = log(1 + img_1.at(i, j)[0]);
imageLog.at(i, j)[1] = log(1 + img_1.at(i, j)[1]);
imageLog.at(i, j)[2] = log(1 + img_1.at(i, j)[2]);
}
}

//归一化到0~255    
normalize(imageLog, imageLog, 0, 255, CV_MINMAX);
//转换成8bit图像显示    
convertScaleAbs(imageLog, imageLog);
imshow("after", imageLog);

//基于伽马变换的图像增强,---------------------------------------------------------------------4
//伽马变换对于图像对比度偏低，并且整体亮度值偏高（对于于相机过曝）情况下的图像增强效果明显。
Mat imageGamma(img_1.size(), CV_32FC3);
for (int i = 0; i < img_1.rows; i++)
{
for (int j = 0; j < img_1.cols; j++)
{
imageGamma.at(i, j)[0] = (img_1.at(i, j)[0])*(img_1.at(i, j)[0])*(img_1.at(i, j)[0]);
imageGamma.at(i, j)[1] = (img_1.at(i, j)[1])*(img_1.at(i, j)[1])*(img_1.at(i, j)[1]);
imageGamma.at(i, j)[2] = (img_1.at(i, j)[2])*(img_1.at(i, j)[2])*(img_1.at(i, j)[2]);
}
}

//归一化到0~255    
normalize(imageGamma, imageGamma, 0, 255, CV_MINMAX);
//转换成8bit图像显示    
convertScaleAbs(imageGamma, imageGamma);
imshow("伽马变换图像增强效果", imageGamma);

//imshow("【图2】", img_2);
cout << "图像1的长宽：" << img_1.rows <<"*"<< img_1.cols << endl;
//cout << "图像2的长宽：" << img_2.rows <<"*"<< img_2.cols << endl;

//转为灰度图并去燥
Mat gray_img;
cvtColor(imageGamma, gray_img, CV_RGB2GRAY);
blur(gray_img,gray_img,Size(5,5));

//canny边缘检测
Mat canny_img;
Canny(gray_img,canny_img,10.0,60.0);

vector compression_params;
//向compression_params里面添加值
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(100);
imwrite("F:\图标\222222增强.jpg", imageGamma, compression_params);
imwrite("F:\图标\222222素描.jpg", canny_img, compression_params);
cout << "素描图像保存成功！"<<endl; 
cout << "增强图像保存成功！"<<endl;
imshow("gray_image", gray_img);
imshow("canny_img", canny_img);
waitkey(0);
return 0;
}