【图像融合】基于DCT算法实现图像融合附matlab代码

 1 简介

针对普通相机拍摄图像中存在区域曝光问题,提出一种改进的基于离散余弦变换的多曝光图像融合算法.分别对同一场景的多幅不同曝光图像分块,并对每个小块进行离散余弦变换.对提取相应位置的低频系数平均加权,获取图像近似部分的融合分量;对高频系数基于区域标准差确定权重系数,根据权重系数的大小获取细节部分的融合分量.最后,通过反离散余弦变换获得较好质量的多曝光融合图像.仿真实验表明,该算法在提取低频系数为25%,高频系数为75%时,展现的图像细节信息更加丰富.

2 部分代码

function[imf] = mrdctf(im1,im2,J)
% Multi resolution 1D DCT based image fusion
% Developed by : VPS Naidu, MSDF Lab
% input: im1 & im2 images to be fused
% J no. of decomposition levels
% output: imf fused image
imfr = mrdctif(im1,im2,J);
imfc = mrdctif(im1',im2',J)';
imf = 0.5*(imfr+imfc);
function[imf] = mrdctif(im1,im2,J)
%Multi resolution image fusion by DCT
[m,n] = size(im1);
x1 = c2dt1d(im1,m,n);
x2 = c2dt1d(im2,m,n);
for i=1:J
X1{i} = mrdct(x1);
X2{i} = mrdct(x2);
x1 = X1{i}.L;
x2 = X2{i}.L;
end
% fusion start here
Xf.L = 0.5*(X1{J}.L+X2{J}.L);
for i=J:-1:1
D = (abs(X1{i}.H) - abs(X2{i}.H)) >=0;
Xf.H = D.*X1{i}.H + (~D).*X2{i}.H;
Xf.L = imrdct(Xf);
end
imf = c1d2d(Xf.L,m,n);
function[R] = c2dt1d(R,m,n)
% conversion from 2D array to 1D vector
R(2:2:end,:)=R(2:2:end,end:-1:1);
R = reshape(R',1,m*n);
function[R] = c1d2d(R,m,n)
% conversion from 1D vector to 2D array
R = reshape(R,n,m)';
R(2:2:end,:)=R(2:2:end,end:-1:1);
function[X] = mrdct(x)
% multi resolution analysis
n=length(x);
Y = dct(x,n);
X.L = idct(Y(1:n/2));% low frequency
X.H = Y(n/2+1:n);% low frequency
function[x] = imrdct(X)
% inverse multi resolution analysis
xl = dct(X.L);
x = [xl X.H];
x = idct(x);

3 仿真结果

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编辑

4 参考文献

[1]刘卫华, 马洋花, 刘颖. 基于DCT变换的多曝光图像融合方法[J]. 西安邮电大学学报, 2016, 21(6):4.

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