理解mulitband。所谓的mulitband，其实就是一种多尺度的样条融合，其实现的主要方法就是laplace金字塔。
高斯金字塔是向下采样，而laplace金字塔式向上采样（也就是恢复），采用的都是差值的方法。如何能够在金字塔各个层次上面进行图像的融合，结果证明是相当不错的。网络上面流传的一个类解释了这个问题，并且能够拿来用：
// GOImage.cpp : 定义? DLL 的?初?始?化例y程。
//
#include "stdafx.h"
#include <iostream>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
using namespace cv;
#ifdef _DEBUG
#define new DEBUG_NEW
#endif
#define DllExport _declspec (dllexport)
/*
1.设计?一?个?mask（一?半?全?1，?一?半?全?0）?，?并计?算?level层?的?gaussion_mask[i]；?
2.计?算?两?幅图?像?每?一?层?的?Laplacian[i]，?并与?gaussion_mask[i]相乘?，?合?成一?幅result_lapacian[i]；?
3.对?两?幅图?像?不?断?求prydown，?并把?最?高?层?保存?在gaussion[i],与?gaussion_mask[i]相乘?，?合?成一?幅result_gaussion;
4,对?result_gaussion不?断?求pryup，?每?一?层?都?与?result_lapacian[i]合?成，?最?后得?到?原-图?像?大小?的?融合?图?像?。
*/
class LaplacianBlending {
private:
Mat_<Vec3f> top;
Mat_<Vec3f> down;
Mat_< float> blendMask;
vector<Mat_<Vec3f> > topLapPyr,downLapPyr,resultLapPyr; //Laplacian Pyramids
Mat topHighestLevel, downHighestLevel, resultHighestLevel;
vector<Mat_<Vec3f> > maskGaussianPyramid; //masks are 3-channels for easier multiplication with RGB
int levels;
//创建金e字?塔t
void buildPyramids() {
//参?数y的?解a释 top就是?top ,topLapPyr就是?top的?laplacian的?pyr,而?topHighestLevel保存?的?是?最?高?端?的?高?斯1金e字?塔t
buildLaplacianPyramid(top,topLapPyr,topHighestLevel);
buildLaplacianPyramid(down,downLapPyr,downHighestLevel);
buildGaussianPyramid();
}
//创建gauss金e字?塔t
void buildGaussianPyramid() {//金e字?塔t内容Y为a每?一?层?的?掩模
assert(topLapPyr.size()>0);
maskGaussianPyramid.clear();
Mat currentImg;
//blendMask就是?掩码?
cvtColor(blendMask, currentImg, CV_GRAY2BGR); //store color img of blend mask into maskGaussianPyramid
maskGaussianPyramid.push_back(currentImg); //0-level
currentImg = blendMask;
for (int l=1; l<levels+1; l++) {
Mat _down;
if (topLapPyr.size() > l)
pyrDown(currentImg, _down, topLapPyr[l].size());
else
pyrDown(currentImg, _down, topHighestLevel.size()); //lowest level
Mat down;
cvtColor(_down, down, CV_GRAY2BGR);
maskGaussianPyramid.push_back(down); //add color blend mask into mask Pyramid
currentImg = _down;
}
}
//创建laplacian金e字?塔t
void buildLaplacianPyramid(const Mat& img, vector<Mat_<Vec3f> >& lapPyr, Mat& HighestLevel) {
lapPyr.clear();
Mat currentImg = img;
for (int l=0; l<levels; l++) {
Mat down,up;
pyrDown(currentImg, down);
pyrUp(down, up,currentImg.size());
Mat lap = currentImg - up; //存?储的?就是?残D差?
lapPyr.push_back(lap);
currentImg = down;
}
currentImg.copyTo(HighestLevel);
}
Mat_<Vec3f> reconstructImgFromLapPyramid() {
//将?左右laplacian图?像?拼成的?resultLapPyr金e字?塔t中D每?一?层?
//从上?到?下?插?值放?大并相加，?即得?blend图?像?结果?
Mat currentImg = resultHighestLevel;
for (int l=levels-1; l>=0; l--) {
Mat up;
pyrUp(currentImg, up, resultLapPyr[l].size());
currentImg = up + resultLapPyr[l];
}
return currentImg;
}
void blendLapPyrs() {
//获?得?每?层?金e字?塔t中D直接用?左右两?图?Laplacian变?换?拼成的?图?像?resultLapPyr
//一?半?的?一?半?就是?在这a个?地?方?计?算?的?。 是?基于掩模的?方?式?进?行D的?.
resultHighestLevel = topHighestLevel.mul(maskGaussianPyramid.back()) +
downHighestLevel.mul(Scalar(1.0,1.0,1.0) - maskGaussianPyramid.back());
for (int l=0; l<levels; l++) {
Mat A = topLapPyr[l].mul(maskGaussianPyramid[l]);
Mat antiMask = Scalar(1.0,1.0,1.0) - maskGaussianPyramid[l];
Mat B = downLapPyr[l].mul(antiMask);
Mat_<Vec3f> blendedLevel = A + B;
resultLapPyr.push_back(blendedLevel);
}
}
public:
LaplacianBlending( const Mat_<Vec3f>& _top, const Mat_<Vec3f>& _down, const Mat_< float>& _blendMask, int _levels)://缺省?数y据Y，?使1用? LaplacianBlending lb(l,r,m,4);
top(_top),down(_down),blendMask(_blendMask),levels(_levels)
{
assert(_top.size() == _down.size());
assert(_top.size() == _blendMask.size());
buildPyramids(); //创建laplacian金e字?塔t和gauss金e字?塔t
blendLapPyrs(); //将?左右金e字?塔t融合?成为a一?个?图?片?
};
Mat_<Vec3f> blend() {
return reconstructImgFromLapPyramid();//reconstruct Image from Laplacian Pyramid
}
};
Mat_<Vec3f> LaplacianBlend( const Mat_<Vec3f>& t, const Mat_<Vec3f>& d, const Mat_< float>& m) {
LaplacianBlending lb(t,d,m,4);
return lb.blend();
}
DllExport double aValue =1.5;
DllExport int dlladd()
{
return 5;
}
DllExport int dlladd( int a,int b)
{
return a+b;
}
DllExport cv::Mat imagetest()
{
cv::Mat image1= cv::imread( "C:\apple.png",1);
cv::Mat image2= cv::imread( "C:\orange.png",1);
Mat_<Vec3f> t; image1.convertTo(t,CV_32F,1.0/255.0); //Vec3f表示?有D三y个?通道，?即 l[row][column][depth]
Mat_<Vec3f> d; image2.convertTo(d,CV_32F,1.0/255.0);
Mat_< float> m(t.rows,d.cols,0.0); //将?m全?部?赋3值为a0
//m(Range::all(),Range(0,m.cols/2)) = 1.0; //原-来初?始?的?掩码?是?在这a里?！?！?
m(Range(0,m.rows/2),Range::all())=1.0;
Mat_<Vec3f> blend = LaplacianBlend(t,d, m);
imshow( "blended",blend);
return blend;
}
需要注意的是， m(Range(0,m.rows/2),Range::all())=1.0表明了原始图像的掩码，这个掩码就是那个分界的地方
