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• W4312280925 abstract "Video deblurring has achieved remarkable progress thanks to the success of deep neural networks. Most methods solve for the deblurring end-to-end with limited information propagation from the video sequence. However, different frame regions exhibit different characteristics and should be provided with corresponding relevant information. To achieve fine-grained deblurring, we designed a memory branch to memorize the blurry-sharp feature pairs in the memory bank, thus providing useful information for the blurry query input. To enrich the memory of our memory bank, we further designed a bidirectional recurrency and multi-scale strategy based on the memory bank. Experimental results demonstrate that our model outperforms other state-of-the-art methods while keeping the model complexity and inference time low. The code is available at https://github.com/jibo27/MemDeblur." @default.
• W4312280925 created "2023-01-04" @default.
• W4312280925 creator A5006278133 @default.
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• W4312280925 date "2022-06-01" @default.
• W4312280925 modified "2023-09-24" @default.
• W4312280925 title "Multi-Scale Memory-Based Video Deblurring" @default.
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• W4312280925 doi "https://doi.org/10.1109/cvpr52688.2022.00196" @default.
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