research-article

Infrared and visible image fusion method based on LatLRR and ICA

Authors:

Xilin WenAuthors Info & Claims

PRIS '21: Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems

Pages 20 - 23

https://doi.org/10.1145/3480651.3480656

Published: 12 October 2021 Publication History

Abstract

To solve the problem of missing lots of texture details in the fusion image, we propose a new fusion method of infrared and visible images based on latent low-rank representation(LatLRR) and independent component analysis(ICA) in this paper. Firstly, the source image is decomposed into low-rank components, sparse components, and noise components by LatLRR. Secondly, ICA is utilized for the low-rank part of infrared image and visible image to obtain the main difference between two source images. Then, the image containing more information is determined by comparing the entropy of two source images and it is employed as a benchmark. Finally, the fused image is accomplished by connecting the benchmark result, the low-rank components, and the sparse components of another image according to the result obtained by ICA. Compared with other fusion methods, experimental results demonstrate that the proposed method has better visual effects and evaluation indicators.

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Cited By

Hao CChen YNing FChou TChen M(2024)Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared ImagesSensors10.3390/s2405151424:5(1514)Online publication date: 26-Feb-2024
https://doi.org/10.3390/s24051514

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PRIS '21: Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems

July 2021

91 pages

ISBN:9781450390392

DOI:10.1145/3480651

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 October 2021

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PRIS 2021

PRIS 2021: 2021 3rd International Conference on Pattern Recognition and Intelligent Systems

July 28 - 30, 2021

Bangkok, Thailand

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Cited By

Hao CChen YNing FChou TChen M(2024)Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared ImagesSensors10.3390/s2405151424:5(1514)Online publication date: 26-Feb-2024
https://doi.org/10.3390/s24051514

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