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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/155072

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155072

Title:	ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation
Authors:	彭彥璁 Peng, Yan-Tsung;Wu, Jia-Hao;Tsai, Fu-Jen;Tsai, Chung-Chi;Lin, Chia-Wen;Lin, Yen-Yu
Contributors:	資訊系
Keywords:	Training;Computer vision;Source coding;Dynamics;Aerospace electronics;Data augmentation;Data models
Date:	2024-06
Issue Date:	2025-01-07 09:36:34 (UTC+8)
Abstract:	Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However, there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure, we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space. This paper proposes Implicit Diffusion-based reBLurring AUgmentation (ID-Blau), utilizing a sharp image paired with a controllable blur condition map to produce a corresponding blurred image. We parameterize the blur patterns of a blurred image with their orientations and magnitudes as a pixel-wise blur condition map to simulate motion trajectories and implicitly represent them in a continuous space. By sampling diverse blur conditions, ID-Blau can generate various blurred images unseen in the training set. Experimental results demonstrate that ID-Blau can produce realistic blurred images for training and thus significantly improve performance for state-of-the-art deblurring models. The source code is available at https://github.com/plusgood-steven/ID-Blau.
Relation:	IEEE / CVF Computer Vision and Pattern Recognition Conference, IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/CVPR52733.2024.02442
DOI:	10.1109/CVPR52733.2024.02442
Appears in Collections:	[資訊科學系] 會議論文

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