1 简介
即无重影多曝光图像融合技术，采用了密集的SIFT描述符和导引滤波器实现图像融合。
2 部分代码
function [ F ] = DSIFT_exposure_fusion(I,scale,mode)
%This is the MATLAB implementation of the multi-exposure fusion algorithm described in the following paper:
%N. Hayat and M. Imran, "Ghost-free Multi Exposure Image Fusion using the Dense SIFT Discriptor and the Guided Filter",
%Inputs:
%I - a color multi-exposure image sequence
%scale - scale factor of dense SIFT, the default value is 16
%mode - controls the fusion mode, static fusion if it is set to 1 and dynamic otherwise
%Output:
%F - the fused image
addpath('Pyramid_Decomposition');
addpath('Guided_Filter');
addpath('Dense_SIFT');
addpath('Color_Dissimilarity');
tic
[H W C N]=size(I);
imgs=double(I)/255;
%
%
imgs_gray=zeros(H,W,N);
for i=1:N
imgs_gray(:,:,i)=rgb2gray(imgs(:,:,:,i));
end
%
% %dense sift calculation
dsifts=zeros(H,W,32,N, 'single');
for i=1:N
img=imgs_gray(:,:,i);
ext_img=img_extend(img,scale/2-1);
[dsifts(:,:,:,i)] = DenseSIFT(ext_img, scale, 1);
end
%local contrast
contrast_map=zeros(H,W,N);
for i=1:N
contrast_map(:,:,i)=sum(dsifts(:,:,:,i),3);
end
% winner-take-all weighted average strategy for local contrast
[x labels]=max(contrast_map,[],3);
clear x;
for i=1:N
mono=zeros(H,W);
mono(labels==i)=1;
contrast_map(:,:,i)=mono;
end
%Brightness Feature
exposure_map=ones(H,W,N);
exposure_map((imgs_gray>=0.90)|(imgs_gray<=0.10))=0;
% Color Dissimilarity Feature (only for dynamic fusion)
I_eq=Heq(double(imgs));
med_map=median(I_eq,4);
cost2=distance_map(I_eq,med_map,1,3,30);
if mode==1
T_map=exposure_map.*cost2.*contrast_map;
else
T_map=exposure_map.*contrast_map;
end
weight_map=T_map;
%weight map refinement using Guided Filter
for i=1:N
weight_map(:,:,i) = fastGF( weight_map(:,:,i),12,0.25,2.5);
end
% normalizing weight maps
%
weight_map = weight_map + 10^-25; %avoids division by zero
weight_map = weight_map./repmat(sum(weight_map,3),[1 1 N]);
% Pyramid Decomposition
% create empty pyramid
pyr = gaussian_pyramid(zeros(H,W,3));
nlev = length(pyr);
% multiresolution blending
for i = 1:N
% construct pyramid from each input image
pyrW = gaussian_pyramid(weight_map(:,:,i));
pyrI = laplacian_pyramid(imgs(:,:,:,i));
% blend
for b = 1:nlev
w = repmat(pyrW{b},[1 1 3]);
pyr{b} = pyr{b} + w .*pyrI{b};
end
end
% reconstruct
F = reconstruct_laplacian_pyramid(pyr);
toc
end
3 仿真结果
4 参考文献
[1]赵轶昌. 基于SIFT和小波多尺度分析的多聚焦图像融合方法研究. Diss. 武汉理工大学, 2013.
Hayat, Naila, and Muhammad Imran. “Ghost-Free Multi Exposure Image Fusion Technique Using Dense {SIFT} Descriptor and Guided Filter.” Journal of Visual Communication and Image Representation, vol. 62, Elsevier {BV}, July 2019, pp. 295–308, doi:10.1016/j.jvcir.2019.06.002.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，有科研问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
