Infrared and Visible Image Fusion Based on Feature Separation
Although a pair of infrared and visible images captured in the same scene have different modes,they also have shared public information and complementary private information.A complete fusion image can be obtained by learning and integrating above information.Inspired by residual network,in the trai...
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Format: | Article |
Language: | zho |
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Editorial office of Computer Science
2022-05-01
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Series: | Jisuanji kexue |
Subjects: | |
Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-58.pdf |
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author | GAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng |
author_facet | GAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng |
author_sort | GAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng |
collection | DOAJ |
description | Although a pair of infrared and visible images captured in the same scene have different modes,they also have shared public information and complementary private information.A complete fusion image can be obtained by learning and integrating above information.Inspired by residual network,in the training stage,each branch is forced to map a label with global features through the interchange and addition of feature-levels among network branches.What’s more,each branch is encouraged to learn the private features of corresponding images.Directly learning the private features of images can avoid designing complex fusion rules and ensure the integrity of feature details.In the fusion stage,the maximum fusion strategy is adopted to fuse the private features,add them to the learned public features at the decoding layer and finally decode the fused image.The model is trained over a multi-focused data set that is synthesized from the NYU-D2 and tested over the real-world TNO data set.Experimental results show that compared with the current mainstream infrared and visible fusion algorithms,the proposed algorithm achieves better results in subjective effects and objective evaluation indicators. |
first_indexed | 2024-04-09T17:33:05Z |
format | Article |
id | doaj.art-f55ada0408114053833c4c0995b547d3 |
institution | Directory Open Access Journal |
issn | 1002-137X |
language | zho |
last_indexed | 2024-04-09T17:33:05Z |
publishDate | 2022-05-01 |
publisher | Editorial office of Computer Science |
record_format | Article |
series | Jisuanji kexue |
spelling | doaj.art-f55ada0408114053833c4c0995b547d32023-04-18T02:35:57ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-05-01495586310.11896/jsjkx.210200148Infrared and Visible Image Fusion Based on Feature SeparationGAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng01 School of Artificial Intelligence and Computer,Jiangsu University,Wuxi,Jiangsu 214122,China ;2 Pattern Recognition and Computational Intelligence Engineering Laboratory,Wuxi,Jiangsu 214122,China ;3 School of Electronics and Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,ChinaAlthough a pair of infrared and visible images captured in the same scene have different modes,they also have shared public information and complementary private information.A complete fusion image can be obtained by learning and integrating above information.Inspired by residual network,in the training stage,each branch is forced to map a label with global features through the interchange and addition of feature-levels among network branches.What’s more,each branch is encouraged to learn the private features of corresponding images.Directly learning the private features of images can avoid designing complex fusion rules and ensure the integrity of feature details.In the fusion stage,the maximum fusion strategy is adopted to fuse the private features,add them to the learned public features at the decoding layer and finally decode the fused image.The model is trained over a multi-focused data set that is synthesized from the NYU-D2 and tested over the real-world TNO data set.Experimental results show that compared with the current mainstream infrared and visible fusion algorithms,the proposed algorithm achieves better results in subjective effects and objective evaluation indicators.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-58.pdfresidual learning|feature extraction|private feature||public feature|image fusion |
spellingShingle | GAO Yuan-hao, LUO Xiao-qing, ZHANG Zhan-cheng Infrared and Visible Image Fusion Based on Feature Separation Jisuanji kexue residual learning|feature extraction|private feature||public feature|image fusion |
title | Infrared and Visible Image Fusion Based on Feature Separation |
title_full | Infrared and Visible Image Fusion Based on Feature Separation |
title_fullStr | Infrared and Visible Image Fusion Based on Feature Separation |
title_full_unstemmed | Infrared and Visible Image Fusion Based on Feature Separation |
title_short | Infrared and Visible Image Fusion Based on Feature Separation |
title_sort | infrared and visible image fusion based on feature separation |
topic | residual learning|feature extraction|private feature||public feature|image fusion |
url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-58.pdf |
work_keys_str_mv | AT gaoyuanhaoluoxiaoqingzhangzhancheng infraredandvisibleimagefusionbasedonfeatureseparation |