Multispectral image fusion technique based on decoupling of information

Abstract To address the issue of unremarkable targets and the serious loss of background details that occurs in multispectral image fusion, a multispectral image fusion method based on information decoupling is proposed. In accordance with the U‐shaped network structure, multi‐scale feature fusion is employed to obtain comprehensive contextual features. Subsequently, taking into account the differences between base and texture information in the images, the recovered image is reconstructed by fusing feature information through the information decoupling branches. Finally, the information decoupling is supervised by a loss function. In addition, a series of comparative experiments are conducted on a self‐constructed multispectral dataset and a publicly available dataset. Experimental results demonstrate that our method not only achieves higher fusion accuracy but also has superior visualization results.

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Publication Details

Journal
IET Image Processing
Published
2024-12-20
DOI
https://doi.org/10.1049/ipr2.13296
Citations
2
Primary Topic
Advanced Image Fusion Techniques
Type
article
Field-Weighted Citation Impact
0.64

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article

Multispectral image fusion technique based on decoupling of information

Wenqi Yu, Jilong Liu, Huilin Wang, Guan Wang
2 citations
IET Image Processing
Advanced Image Fusion Techniques
0.64
article

Multispectral image fusion technique based on decoupling of information

Wenqi Yu, Jilong Liu, Huilin Wang, Guan Wang
article en
2 citations

Abstract

Abstract To address the issue of unremarkable targets and the serious loss of background details that occurs in multispectral image fusion, a multispectral image fusion method based on information decoupling is proposed. In accordance with the U‐shaped network structure, multi‐scale feature fusion is employed to obtain comprehensive contextual features. Subsequently, taking into account the differences between base and texture information in the images, the recovered image is reconstructed by fusing feature information through the information decoupling branches. Finally, the information decoupling is supervised by a loss function. In addition, a series of comparative experiments are conducted on a self‐constructed multispectral dataset and a publicly available dataset. Experimental results demonstrate that our method not only achieves higher fusion accuracy but also has superior visualization results.

IET Image ProcessingVol. 19(1)
Xi'an Institute of Optics and Precision Mechanics (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 25%
Advanced Image Fusion Techniques
0.64
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