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• W4312291490 abstract "Deep learning-based burst super-resolution (SR) approaches are extensively studied in recent years, prevailing in the synthetic datasets and the real datasets. However, the existing networks rarely pay attention to the enhanced denoising problem in raw domain and they are not sufficient to restore complex texture relationships between frames. In this paper, we propose a new framework named A RAW Burst Super-Resolution Method with Enhanced Denoising (EDRBSR), which solves the BurstSR problem by jointly denoising structure and reconstruction enhancement structure. We adopt a Denoising Network to further improve the performance of noise-free SR images. Also, we propose a Reconstruction Network to enhance spatial feature representation and eliminate the influence of spatial noise. In addition, we introduce a new pipeline to compensate for lost information. Experimental results demonstrate that our method over the existing state-of-the-art in both synthetic datasets and real datasets. Furthermore, our approach takes the 5th place in synthetic track of the NTIRE 2022 Burst Super-Resolution Challenge." @default.
• W4312291490 created "2023-01-04" @default.
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• W4312291490 date "2022-01-01" @default.
• W4312291490 modified "2023-09-26" @default.
• W4312291490 title "A RAW Burst Super-Resolution Method with Enhanced Denoising" @default.
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• W4312291490 doi "https://doi.org/10.1007/978-3-031-18916-6_9" @default.
• W4312291490 hasPublicationYear "2022" @default.
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