Python+OpenCV基于小波变换的图像融合

此次导师给安排的任务是将下面两幅图像融合成一幅清晰的图像，根据图像可以看出第一幅图像左清晰右模糊，第二幅图像相反，通过图像融合将两幅图像融合为一幅左右都清晰的图像。


代码如下：

import numpy as np
import cv2 as cv

def fangcha(img):
    row=img.shape[0]
    col=img.shape[1]
    varImg=np.zeros([row,col])
    for i in range(row):#求取求方差范围
        for j in range(col):
            if i-5>0:
                up=i-5
            else:
               up=0
            if i+5<row:
                down=i+5
            else:
                down=row
            if j-5>0:
                left=j-5
            else:
                left=0
            if j+5<col:
                right=j+5
            else:
                right=col
            window=img[up:down,left:right]
            mean,var=cv.meanStdDev(window)#调用OpenCV函数求取均值和方差
            varImg[i,j]=var
    return varImg

def qiuquan(img1,img2):
    row=img1.shape[0]
    col=img1.shape[1]
    array1=fangcha(img1)#调用求方差函数
    array2=fangcha(img2)
    for i in range(row):#求权
        for j in range(col):
            weight1=array1[i,j]/(array1[i,j]+array2[i,j])
            weight2=array2[i,j]/(array1[i,j]+array2[i,j])
            array1[i,j]=weight1
            array2[i,j]=weight2
    return array1,array2

def ronghe(img1,img2):
    cc = img1.copy()
    b,g,r=cv.split(img1)#分通道处理
    b1,g1,r1=cv.split(img2)
    weight1,weight2=qiuquan(b,b1)#调用求权重函数
    weight11,weight22=qiuquan(g,g1)
    weight111,weight222=qiuquan(r,r1)
    new_img=img1*1
    row=new_img.shape[0]
    col=new_img.shape[1]
    b2,g2,r2 = cv.split(cc)
    for i in range(row):#图像融合
        for j in range(col):
            b2[i,j]=(weight1[i,j]*b[i,j]+weight2[i,j]*b1[i,j]).astype(int)
            g2[i,j]=(weight11[i,j]*g[i,j]+weight22[i,j]*g1[i,j]).astype(int)
            r2[i,j]=(weight111[i,j]*r[i,j]+weight222[i,j]*r1[i,j]).astype(int)
    new_img=cv.merge([b2,g2,r2])#通道合并
    return new_img

img1=cv.imread("D:/s1.jpg")
img2=cv.imread("D:/s2.jpg")
img3=ronghe(img1,img2)
cv.imshow("img1",img1)
cv.imshow("img2",img2)
cv.imshow("img3",img3)
cv.waitKey(0)
cv.destroyAllWindows()

编写原理：
1 将两幅图片分通道处理；
2 分别取两幅图片的RGB图；
3 例如，将两幅图像的R通道图进行求权处理，然后将各个图像的权值乘像素点灰度值相加；
4 GB通道图同理。