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• W3097900310 abstract "Video deblurring is still a challenging low-level vision task since spatio-temporal characteristics across both the spatial and temporal domains are difficult to model. In this article, to model the temporal information, we develop a non-local block which estimates inter-frame similarity and inter-frame difference. Specially, for modeling the spatial characteristics and restoring sharp frame details, we propose a recursive block that iteratively refines feature maps generated at the last iteration. In addition, a novel temporal loss function is introduced to ensure the temporal consistency of generated frames. Experimental results on public datasets demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively." @default.
• W3097900310 created "2020-11-09" @default.
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• W3097900310 date "2021-08-01" @default.
• W3097900310 modified "2023-10-17" @default.
• W3097900310 title "Recursive Neural Network for Video Deblurring" @default.
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• W3097900310 doi "https://doi.org/10.1109/tcsvt.2020.3035722" @default.
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