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⛄ 内容介绍
由于不同成像设备的原理不同,对同一目标拍摄所得图像就具有不同的特点.通过一定的算法将这些图像融合在一起,得到的合成图像就具有这些源图像上特有的优点,可以提供更加丰富的内容信息,方便人们的进一步研究,能更有效的分析目标数据.因此,研究图像融合技术具有重要的意义.
⛄ 部分代码
% This code is the implementation of "Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach"
%Cite this article as:
% Bavirisetti, D.P., Xiao, G., Zhao, J. et al. Circuits Syst Signal Process (2019).%https://doi.org/10.1007/s00034-019-01131-z
%%
clc;
clear all;
close all;
% Guided image filter parameters
r=9;eps=10^3;
%% load source images
I1=double(imread('source24_1.tif'));
I2=double(imread('source24_2.tif'));
%% apply multi-scale guided image fusion on source images
tic
F = fuse_MGF(I1, I2, r, eps);
toc
%% display source images and the fused image
figure, imshow((F),[]);
%% apply image contrast enhancement techniques to further enhance the fused image quality
% we either implemented them or used built-in matlab functions.
%They are
%1) Histogram Equalization (HE),
%2) (Bi-histogram equalization) BHE,
%3) Recursive mean-separate histogram equalization (RMSHE),
%4) Bi-bi-histogram equalization with variable enhancement degree (BBHEwVED),
%5) Recursively separated and weighted histogram equalization (RSWHE)
%6) Contrast Limited Adaptive Histogram Equalization (CLAHE).
%References:
%[1] R. Gonzalez and R.Woods, 揟he book�, Digital Image Processing, 2002.
%[2] Y. Kim, 揅ontrast enhancement using brightness preserving bi-histogram equalization�, IEEE Transactions on Consumer Electronics, vol. 43, no 1, pp 1-8, 2002.
%[3] S. Chen and A. Ramli, 揅ontrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,� IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, 2003.
%[4] K. Murahira, T.Kawakami and A. Taguchi, 揗odified histogram equalization for image contrast enhancement�, in 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2010, IEEE, pp. 1-5.
%[5] M. Kim and M. Chung, 揜ecursively separated and weighted histogram equalization for brightness preservation and contrast enhancement�, IEEE Transactions on Consumer Electronics, vol. 54, no.3, pp. 1389-1397, 2008.
%[6] Zuiderveld, Karel. "Contrast Limited Adaptive Histogram Equalization." Graphic Gems IV. San Diego: Academic Press Professional, 1994. 474�485.
% Contrast Limited Adaptive Histogram Equalization (CLAHE). (MATLAB built-in function)
F1 = adapthisteq(uint8(F));
% Histogram Equalization (HE) (MATLAB built-in function)
F2 = histeq(uint8(F));
% Bi-histogram equalization) BHE (Implemented).
F3 = BHEg_gray(uint8(F));
% Recursive mean-separate histogram equalization (RMSHE) (Implemented)
F4= RMSHEg_gray(uint8(F));
%Bi-bi-histogram equalization with variable enhancement degree (BBHEwVED) (Implemented)
F5 = BBHEwVEDg_gray(uint8(F));
% Recursively separated and weighted histogram equalization (RSWHE) (Implemented)
F6 = RSWHEg_gray(uint8(F));
figure(4), imshow((F1),[]);
figure(5), imshow((F2),[]);
figure(6), imshow((F4),[]);
figure(7), imshow((F5),[]);
figure(8), imshow((F6),[]);
figure(9), imshow((F3),[]);
figure, subplot(131);imshow((I1), []);title('图1')
subplot(132);imshow((I2), []);title('图2')
subplot(133);imshow((F3), []);title('融合图')
⛄ 运行结果
⛄ 参考文献
[1]任梦乔. 基于多尺度分析与引导滤波的图像融合算法研究[D]. 西安电子科技大学, 2015.
