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Remote Sensing
Volume 16
Issue 10
10.3390/rs16101795
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Open AccessArticle
by Qiancheng Wei SciProfiles Scilit Preprints.org Google Scholar Ying Liu SciProfiles Scilit Preprints.org Google Scholar Xiaoping Jiang SciProfiles Scilit Preprints.org Google Scholar Ben Zhang SciProfiles Scilit Preprints.org Google Scholar Qiya Su SciProfiles Scilit Preprints.org Google Scholar Muyao Yu SciProfiles Scilit Preprints.org Google Scholar 
Qiancheng WeiYing LiuXiaoping JiangBen ZhangQiya SuMuyao Yu
1
University of Chinese Academy of Sciences, Beijing 101408, China
2
Beijing Institute of Remote Sensing Equipment, Beijing 100005, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(10), 1795; https://doi.org/10.3390/rs16101795
Submission received: 21 March 2024 / Revised: 9 May 2024 / Accepted: 15 May 2024 / Published: 18 May 2024
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
Abstract
The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF,
, FMI, and
metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are
,
,
,
,
, and
higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods.
Keywords: infrared image; visible image; image fusion; multi-modality; attention
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MDPI and ACS Style
Wei, Q.; Liu, Y.; Jiang, X.; Zhang, B.; Su, Q.; Yu, M. DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion. Remote Sens. 2024, 16, 1795. https://doi.org/10.3390/rs16101795
AMA Style
Wei Q, Liu Y, Jiang X, Zhang B, Su Q, Yu M. DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion. Remote Sensing. 2024; 16(10):1795. https://doi.org/10.3390/rs16101795
Chicago/Turabian Style
Wei, Qiancheng, Ying Liu, Xiaoping Jiang, Ben Zhang, Qiya Su, and Muyao Yu. 2024. "DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion" Remote Sensing 16, no. 10: 1795. https://doi.org/10.3390/rs16101795
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Wei, Q.; Liu, Y.; Jiang, X.; Zhang, B.; Su, Q.; Yu, M. DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion. Remote Sens. 2024, 16, 1795. https://doi.org/10.3390/rs16101795
AMA Style
Wei Q, Liu Y, Jiang X, Zhang B, Su Q, Yu M. DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion. Remote Sensing. 2024; 16(10):1795. https://doi.org/10.3390/rs16101795
Chicago/Turabian Style
Wei, Qiancheng, Ying Liu, Xiaoping Jiang, Ben Zhang, Qiya Su, and Muyao Yu. 2024. "DDFNet-A: Attention-Based Dual-Branch Feature Decomposition Fusion Network for Infrared and Visible Image Fusion" Remote Sensing 16, no. 10: 1795. https://doi.org/10.3390/rs16101795
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Remote Sens., EISSN 2072-4292, Published by MDPI
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