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7 March 2024 Residual multi-scale pixel attention fusion network for image deblurring
Zezheng Li, Guoliang Xiang, Ting Shu, Mingjuan Yang, Xia Hua
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Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 1308605 (2024) https://doi.org/10.1117/12.2690098
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China
Abstract
The applications of multi-scale fusion strategy to feature are very effective and common in image restoration. However, there is a lack of research on the application of the multi-scale fusion strategy to attention mechanism, although attention mechanism has also been verified to be effective in image restoration. To address this problem, we propose a residual multi-scale pixel attention fusion block (RMPAFB) to refine the input feature, which successfully combine the multi-scale fusion strategy with pixel attention. RMPAFB can capture feature correspondences from multi-scale pixel attention map, which can be more effective for feature refinement than single-scale pixel attention map. Based on RMPAFB, we build an efficient and effective network called RMPAFNet for image deblurring. Substantial experiments on several benchmark datasets have showed that multi-scale pixel attention performs better than single-scale pixel attention and our proposed RMPAFNet achieves state-of-the-art performance while requiring fewer overheads than recent competing deblurring models.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zezheng Li, Guoliang Xiang, Ting Shu, Mingjuan Yang, and Xia Hua "Residual multi-scale pixel attention fusion network for image deblurring", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 1308605 (7 March 2024); https://doi.org/10.1117/12.2690098
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KEYWORDS
Deblurring
Image fusion
Image restoration
Education and training
Convolution
Feature fusion
Image sharpness
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