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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/149883

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/149883

Title:	Meta Transferring for Deblurring
Authors:	彭彥璁 Peng, Yan-Tsung;Liu, Po-Sheng;Tsai, Fu-Jen;Tsai, Chung-Chi;Lin, Chia-Wen;Lin, Yen-Yu
Contributors:	資訊系
Date:	2022-11
Issue Date:	2024-02-16 15:36:54 (UTC+8)
Abstract:	Previous deblurring methods devote to training a generic model with blur and sharp training pairs. However, these methods might lead to sub-optimal results caused by the domain gap between the training and testing set. In this paper, we proposed a reblur-deblur meta-transferring scheme to realize test-time adaptation for the dynamic scene deblurring. Since blur and sharp pairs are hard to obtain during testing, we leverage blurred videos to find some relative-sharp patches as pseudo ground truths, which would be reblurred by a reblurring model to form pseudo blur and sharp pairs. Our pseudo pairs can enable meta-learning to achieve test-time adaptation with few gradien updates. Extensive experimental results show that our reblur-deblur meta-learning scheme improves the existing deblurring models in various datasets, including, DVD, REDS, and RealBlur.
Relation:	British Machine Vision Conference (BMVC), The British Machine Vision Association and Society for Pattern Recognition
Data Type:	conference
Appears in Collections:	[資訊科學系] 會議論文

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