政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149883
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    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:[Department of Computer Science ] Proceedings

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