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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3242824
|2 doi
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|a pubmed24n1184.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Luo, Jun
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Multi-Exposure Image Fusion via Deformable Self-Attention
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| 264 |
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1 |
|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 06.04.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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| 520 |
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|a Most multi-exposure image fusion (MEF) methods perform unidirectional alignment within limited and local regions, which ignore the effects of augmented locations and preserve deficient global features. In this work, we propose a multi-scale bidirectional alignment network via deformable self-attention to perform adaptive image fusion. The proposed network exploits differently exposed images and aligns them to the normal exposure in varying degrees. Specifically, we design a novel deformable self-attention module that considers variant long-distance attention and interaction and implements the bidirectional alignment for image fusion. To realize adaptive feature alignment, we employ a learnable weighted summation of different inputs and predict the offsets in the deformable self-attention module, which facilitates that the model generalizes well in various scenes. In addition, the multi-scale feature extraction strategy makes the features across different scales complementary and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art MEF methods
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Ren, Wenqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Xinwei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Xiaochun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2023) vom: 10. Feb.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:PP
|g year:2023
|g day:10
|g month:02
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|u http://dx.doi.org/10.1109/TIP.2023.3242824
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