Ms. Mingjuan Yang Profile

Mingjuan Yang

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Proceedings Article | 7 March 2024 Paper

Residual multi-scale pixel attention fusion network for image deblurring

Zezheng Li, Guoliang Xiang, Ting Shu, Mingjuan Yang, Xia Hua

Proceedings Volume 13086, 1308605 (2024) https://doi.org/10.1117/12.2690098

KEYWORDS: Deblurring, Image fusion, Image restoration, Education and training, Convolution, Feature fusion, Performance modeling, Image sharpness, Visualization, Image processing

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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.

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