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    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/155071


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155071


    Title: Domain-adaptive Video Deblurring via Test-time Blurring
    Authors: 彭彥璁
    Peng, Yan-Tsung;He, Jin-Ting;Tsai, Fu-Jen;Wu, Jia-Hao;Tsai, Chung-Chi;Lin, Chia-Wen;Lin, Yen-Yu
    Contributors: 資訊系
    Keywords: Video deblurring;Domain adaptation;Diffusion model
    Date: 2024-09
    Issue Date: 2025-01-07 09:36:33 (UTC+8)
    Abstract: Dynamic scene video deblurring aims to remove undesirable blurry artifacts captured during the exposure process. Although previous video deblurring methods have achieved impressive results, they suffer from significant performance drops due to the domain gap between training and testing videos, especially for those captured in real-world scenarios. To address this issue, we propose a domain adaptation scheme based on a blurring model to achieve test-time fine-tuning for deblurring models in unseen domains. Since blurred and sharp pairs are unavailable for fine-tuning during inference, our scheme can generate domain-adaptive training pairs to calibrate a deblurring model for the target domain. First, a Relative Sharpness Detection Module is proposed to identify relatively sharp regions from the blurry input images and regard them as pseudo-sharp images. Next, we utilize a blurring model to produce blurred images based on the pseudo-sharp images extracted during testing. To synthesize blurred images in compliance with the target data distribution, we propose a Domain-adaptive Blur Condition Generation Module to create domain-specific blur conditions for the blurring model. Finally, the generated pseudo-sharp and blurred pairs are used to fine-tune a deblurring model for better performance. Extensive experimental results demonstrate that our approach can significantly improve state-of-the-art video deblurring methods, providing performance gains of up to 7.54dB on various real-world video deblurring datasets. The source code is available at https://github.com/Jin-Ting-He/DADeblur.
    Relation: European Conference on Computer Vision (ECCV), Springer Science+Business Media
    Data Type: conference
    DOI 連結: https://doi.org/10.1007/978-3-031-73404-5_8
    DOI: 10.1007/978-3-031-73404-5_8
    Appears in Collections:[資訊科學系] 會議論文

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