实现haar小波
参考文章中讲解了haar小波的原理，比较易懂，实现了多级haar小波分解，本文参照它进行了练习，同时添加了小波重建。
# include<iostream># include<opencv2/opencv.hpp>using namespace std;
using namespace cv;int main()
{
Mat src = imread("F:\\testdata\\pic\\16.jpg");
Mat HSV;
cvtColor(src, HSV, CV_BGR2HSV);
vector<Mat> channels;
split(HSV, channels);
Mat img = channels.at(1); //上述是获取HSV中的饱和度图像，按需修改
namedWindow("img", 0);
imshow("img", img); int height = img.rows; int width = img.cols; int depth = 2; int depthcount = 1;
Mat tmp = Mat::ones(Size(width, height), CV_32FC1);
Mat wavelet = Mat::ones(Size(width, height), CV_32FC1);
Mat imgtmp = img.clone();
imgtmp.convertTo(imgtmp, CV_32FC1); //---------------------------------------小波分解-----------------------------//
while (depthcount <= depth)
{
height = img.rows / pow(2,depthcount-1);
width = img.cols / pow(2,depthcount-1); //水平方向变换
for (int i = 0; i < height; ++i)
{ for (int j = 0; j < width / 2; ++j)
{
tmp.at<float>(i, j) = (imgtmp.at<float>(i, 2 * j) + imgtmp.at<float>(i, 2 * j + 1)) / 2;
tmp.at<float>(i, j+width/2) = (imgtmp.at<float>(i, 2 * j) - imgtmp.at<float>(i, 2 * j + 1)) / 2;
}
}
//垂直方向变换
for (int i = 0; i < height / 2; ++i)
{ for (int j = 0; j < width; ++j)
{
wavelet.at<float>(i, j) = (tmp.at<float>(2 * i, j) + tmp.at<float>(2 * i + 1, j)) / 2;
wavelet.at<float>(i+height/2, j) = (tmp.at<float>(2 * i, j) - tmp.at<float>(2 * i + 1, j)) / 2;
}
} /*
//低通滤波，选用高斯低通滤波器
Mat ROI = wavelet(Rect(0, 0, width, height));
GaussianBlur(ROI, ROI, Size(7, 7), 0.5);
Mat dst(wavelet, Rect(0, 0, width, height));
ROI.copyTo(dst);
*/
imgtmp = wavelet;
depthcount++;
}
//------------------------------------------小波重建--------------------------------------//
while (depth > 0)
{
height = img.rows / pow(2, depth - 1);
width = img.cols / pow(2, depth - 1); //列逆变换
for (int i = 0; i < height/2; ++i)
{ for (int j = 0; j < width; ++j)
{ float value1 = imgtmp.at<float>(i, j); float value2 = imgtmp.at<float>(i+height/2, j);
tmp.at<float>(2 * i, j) = value1 + value2;
tmp.at<float>(2 * i+1, j) = value1 - value2;
}
}
//行逆变换
for (int i = 0; i < height; ++i)
{ for (int j = 0; j < width / 2; ++j)
{ float value1 = tmp.at<float>(i, j); float value2 = tmp.at<float>(i , j+width/2);
wavelet.at<float>(i, 2*j) = value1 + value2;
wavelet.at<float>(i , 2*j+1) = value1 - value2;
}
}
Mat ROI=wavelet(Rect(0, 0, width, height));
Mat dst(imgtmp, Rect(0, 0, width, height));
ROI.copyTo(dst);
depth--;
}
namedWindow("res", 0);
imgtmp.convertTo(imgtmp, CV_8UC1);
imshow("res", imgtmp);
waitKey(0); return 0;
}开源代码实现多种小波函数的小波变换
可以选择按照其文档中的配置方法，调用动态链接库，但是我的尝试不成功，出现了内存异常，所以本文直接将其源码放入自己的项目中，和自己的项目一起编译，可以实现同样的功能。需要注意的是，需要配置好FFTW这个依赖，方法见VS2015配置FFTW
利用这个库中的函数可以得到小波分解的
近似系数和细节系数
使用示例
重要的只有如下两行函数调用
dwt_2d(data, depth, name, output, flag, length); idwt_2d(output, flag, name, res, length);
#include <iostream>#include <fstream>#include "wavelet2d.h"#include <vector>#include <string>#include <cmath>#include<opencv2\opencv.hpp>using namespace std;
using namespace cv;int main() { //读入图片，获取饱和度
Mat src = imread("F:\\testdata\\poreImgV3\\10.jpg");
resize(src, src, Size(450, 450));
Mat HSV;
cvtColor(src, HSV, CV_BGR2HSV);
vector<Mat> channels;
channels.resize(3);
split(HSV, channels);
Mat saturation = channels.at(1);
namedWindow("s", 0);
imshow("s", saturation);
waitKey(0); //将饱和度图像数据存入vector<vector<double>>中,一行一行放置
int row = saturation.rows; int col = saturation.cols; //分配大小
vector<vector<double>> data;
data.resize(row); for (int i = 0; i < data.size(); ++i)
{
data[i].resize(col);
} //放入数据
for (int i = 0; i < row; ++i)
{ for (int j = 0; j < col; ++j)
{
data[i][j] = saturation.at<uchar>(i, j);
}
} int depth = 2;
string name = "haar";
vector<double> output; //output.resize(row*col);
vector<double> flag;
vector<int> length;
//小波分解
dwt_2d(data, depth, name, output, flag, length);
vector<vector<double>> res;
res.resize(row); for (int i = 0; i < res.size(); ++i)
{
res[i].resize(col);
} //----------------------------------------------------------------------------------
//小波重建
idwt_2d(output, flag, name, res, length); //写回原图像中
Mat recovery(src.size(), CV_8UC1); for (int i = 0; i < row; ++i)
{ for (int j = 0; j < col; ++j)
{
recovery.at<uchar>(i, j) = (uchar)res[i][j];
}
}
imshow("res", recovery);
waitKey(0);
system("pause"); return 0;
}
作者：kuizhu
链接：https://www.jianshu.com/p/69f3d0fa4bed