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Content introduction
In today’s digital image processing field, merging different types of images has become a popular research direction. Fusion of infrared and visible light images, multi-focus images, multi-modal medical images and multi-exposure images is one of the important technologies. These image fusion technologies have wide applications in many fields, including military, medicine, security, and computer vision.
Infrared and visible light image fusion is to fuse infrared images and visible light images together to obtain more comprehensive and accurate information. Infrared images can provide information on the heat distribution of the target, while visible light images can provide information on the shape and color of the target. Fusing the two images together can yield more detailed target information, which can help applications such as military target detection, night surveillance, and fire detection.
Multi-focus image fusion is to fuse images at different focuses to obtain a larger depth of field range. In traditional photography, only one focus point is sharp and the rest is blurred. Multi-focus image fusion technology can fuse images under multiple focuses so that the entire image is clearly visible. This has important implications for fields such as medical imaging, microscopy images, and machine vision.
Multimodal medical image fusion is the fusion of images from different medical imaging modalities to provide more comprehensive and accurate diagnostic information. Medical imaging usually includes images from different modalities such as X-rays, CT scans, MRI and PET. Fusing these images together can comprehensively utilize their respective advantages to improve the accuracy and reliability of disease diagnosis.
Multi-exposure image fusion is to fuse images under different exposures together to obtain a larger dynamic range. In photography, underexposure or overexposure often occurs when the scene you are shooting contains both bright and dark parts. Multi-exposure image fusion technology can fuse images under different exposures so that the details of the entire image can be clearly seen. This is useful for things like photography, computer vision, and image enhancement
Part of the code
function [plaza, v, time,buspla] = create_plaza(B, L, plazalength)</code><code>%</code><code>% create_plaza create the empty plaza matrix( no car ). </code><code>% 1 = car, 0 = empty, -1 = forbid, -3 = empty & amp;booth</code><code>%</code><code>% USAGE: [plaza, v , time] = create_plaza(B, L, plazalength)</code><code>% B = number booths</code><code>% L = number lanes in highway before and after plaza</code><code>% plazalength = length of the plaza</code><code>%</code><code>% zhou lvwen: [email protected]</code><code>?</code><code>? </code><code>plaza = zeros(plazalength,B + 2); % 1 = car, 0 = empty, -1 = forbid, -3 = empty & amp;booth</code><code>v = zeros (plazalength,B + 2); % velocity of automata (i,j), if it exists</code><code>time = zeros(plazalength,B + 2); % cost time of automata (i,j) if it exists</code><code>?</code><code>plaza(1:plazalength,[1,2 + B]) = -1;</code><code>plaza(ceil(plazalength/2) ,[3:1 + B]) =-1;</code><code>%left: angle of width decline for boundaries</code><code>toptheta = 1.3; </code><code>bottomtheta = 1.2 ;</code><code>?</code><code>for col = 2:ceil(B/2-L/2) + 1</code><code> for row = 1:(plazalength-1) /2 - floor(tan(toptheta) * (col-1))</code><code> plaza(row, col) = -1;</code><code> end</code><code> for row = 1:(plazalength-1)/2 - floor(tan(bottomtheta) * (col-1))</code><code> plaza(plazalength + 1-row, col) = -1;</code><code> end</code><code>end</code><code>?</code><code>fac = ceil(B/2-L/2)/floor(B/2-L/2); </code><code>%right: angle of width decline for boundaries</code><code>toptheta = atan(fac*tan(toptheta));</code><code>bottomtheta = atan(fac*tan( bottomtheta));</code><code>?</code><code>for col = 2:floor(B/2-L/2) + 1</code><code> for row = 1:(plazalength -1)/2 - floor(tan(toptheta) * (col-1))</code><code> plaza(row,B + 3-col) = -1;</code><code> end</code> code><code> for row = 1:(plazalength-1)/2 - floor(tan(bottomtheta) * (col-1))</code><code> plaza(plazalength + 1-row,B + 3- col) = -1;</code><code> end</code><code>end</code><code>buspla=plaza;</code><code>?
Operation results
References
[1] Dong Xia. Research on multi-modal brain image fusion method based on sparse representation[D]. North China University[2023-10-05].DOI:CNKI:CDMD:2.1018.183751.
[2] Chen Wen, Yu Yun, Zhou Meihong. Multi-modal medical image fusion simulation based on Matlab [J]. Electronic Technology and Software Engineering, 2017(2):2.DOI:CNKI:SUN:DZRU.0.2017-02- 067.
[3] Wang Han, Liu Jianghao. A multi-modal fog removal method based on visible-far infrared images: 202210141500[P][2023-10-05].