基于opencv的小波变换代码和图像结果

 #include "stdafx.h"
 #include "WaveTransform.h"
 #include <math.h>
 #include <imgproc/imgproc.hpp>

 Mat WaveTransform::WDT(const Mat &_src,const string _wname,const int _level)
 {
     Mat src=Mat_<float>(_src);
     Mat dst=Mat::zeros(src.rows,src.cols,src.type());
     int N=src.rows;
     int D=src.cols;
     //高通低通滤波器
     Mat lowFilter;
     Mat highFilter;
     wavelet(_wname,lowFilter,highFilter);
     //小波变换
     int t=;
     int row=N;
     int col=D;
     while (t<=_level)
     {
         //先进行 行小波变换
         for (int i=;i<row;i++)
         {
             //取出src中要处理的数据的一行
             Mat oneRow=Mat::zeros(,col,src.type());
             for (int j=;j<col;j++)
             {
                 oneRow.at<float>(,j)=src.at<float>(i,j);
             }
             oneRow=waveletDecompose(oneRow,lowFilter,highFilter);
             for (int j=;j<col;j++)
             {
                 dst.at<float>(i,j)=oneRow.at<float>(,j);
             }
         }
         char s[];
         itoa(t,s,);
         imshow(s,dst);
         waitKey();
 #if 0
     //    normalize(dst,dst,0,255,NORM_MINMAX);
         IplImage dstImg1=IplImage(dst);
         cvSaveImage("dst1.jpg",&dstImg1);
 #endif

         //小波列变换
         for (int j=;j<col;j++)
         {
             Mat oneCol=Mat::zeros(row,,src.type());
             for (int i=;i<row;i++)
             {
                 oneCol.at<float>(i,)=dst.at<float>(i,j);//dst,not src
             }
             oneCol=(waveletDecompose(oneCol.t(),lowFilter,highFilter)).t();
             for (int i=;i<row;i++)
             {
                 dst.at<float>(i,j)=oneCol.at<float>(i,);
             }
         }
 #if 0
         //    normalize(dst,dst,0,255,NORM_MINMAX);
         IplImage dstImg2=IplImage(dst);
         cvSaveImage("dst2.jpg",&dstImg2);
 #endif
         //更新
         row/=;
         col/=;
         t++;
         src=dst;

     }
     return dst;
 }
 
 //生成不同类型的小波
 void WaveTransform::wavelet( const string _wname, Mat &_lowFilter, Mat &_highFilter )
 {

     if (_wname=="haar" || _wname=="db1")
     {
         int N=;
         _lowFilter=Mat::zeros(,N,CV_32F);
         _highFilter=Mat::zeros(,N,CV_32F);

         _lowFilter.at<float>(,)=/sqrtf(N);
         _lowFilter.at<float>(,)=/sqrtf(N);

         _highFilter.at<float>(,)=-/sqrtf(N);
         _highFilter.at<float>(,)=/sqrtf(N);
     }
     if (_wname=="sym2")
     {
         int N=;
         float h[]={-0.483, 0.836, -0.224, -0.129};
         float l[]={-0.129, 0.224,    0.837, 0.483};

         _lowFilter=Mat::zeros(,N,CV_32F);
         _highFilter=Mat::zeros(,N,CV_32F);

         for (int i=;i<N;i++)
         {
             _lowFilter.at<float>(,i)=l[i];
             _highFilter.at<float>(,i)=h[i];
         }
     }

 }
 
 //小波分解
 Mat WaveTransform::waveletDecompose( const Mat &_src, const Mat &_lowFilter, const Mat &_highFilter )
 {
     assert(_src.rows== && _lowFilter.rows== && _highFilter.rows ==);
     assert(_src.cols>=_lowFilter.cols && _src.cols>=_highFilter.cols );
     Mat &src=Mat_<float>(_src);

     int D=src.cols;

     Mat &lowFilter=Mat_<float>(_lowFilter);
     Mat &highFilter=Mat_<float>(_highFilter);

     //频域滤波或时域卷积；ifft( fft(x) * fft(filter)) = cov(x,filter)
     Mat dst1=Mat::zeros(,D,src.type());
     Mat dst2=Mat::zeros(,D,src.type());

     filter2D(src,dst1,-,lowFilter);
     filter2D(src,dst2,-,highFilter);

     //下采样
     Mat downDst1=Mat::zeros(,D/,src.type());
     Mat downDst2=Mat::zeros(,D/,src.type());

     resize(dst1,downDst1,downDst1.size());
     resize(dst2,downDst2,downDst2.size());

     //数据拼接
     for (int i=;i<D/;i++)
     {
         src.at<float>(,i)=downDst1.at<float>(,i);
         src.at<float>(,i+D/)=downDst2.at<float>(,i);

     }
     return src;
 }

用main函数调用WDT

Mat imgWave=m_waveTransform.WDT(src,"haar",);
imshow("img",Mat_<uchar>(imgWave));
waitKey();

用一张考拉图来分析小波变换过程：

原图：

3层小波变换结果图（图片有压缩）：

下面逐一分析：

1 只做完小波行变换

2 行列变换

3 再次行变换

4 再次行列变换

三次之后到最后的分解图。

代码来自：http://shijuanfeng.blogbus.com/logs/221385402.html