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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/149884

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/149884

Title:	Rain2Avoid: Self-Supervised Single Image Deraining
Authors:	彭彥璁 Peng, Yan-Tsung;Li, Wei-Hua
Contributors:	資訊系
Keywords:	Image deraining;self-supervised;stochastic derained references
Date:	2023-06
Issue Date:	2024-02-16 15:36:56 (UTC+8)
Abstract:	The single image deraining task aims to remove rain from a single image, attracting much attention in the field. Recent research on this topic primarily focuses on discriminative deep learning methods, which train models on rainy images with their clean counterparts. However, collecting such paired images for training takes much work. Thus, we present Rain2Avoid (R2A), a training scheme that requires only rainy images for image deraining. We propose a locally dominant gradient prior to reveal possible rain streaks and overlook those rain pixels while training with the input rainy image directly. Understandably, R2A may not perform as well as deraining methods that supervise their models with rain-free ground truth. However, R2A favors when training image pairs are unavailable and can self-supervise only one rainy image for deraining. Experimental results show that the proposed method performs favorably against state-of-the-art few-shot deraining and self-supervised denoising methods.
Relation:	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/ICASSP49357.2023.10097092
DOI:	10.1109/ICASSP49357.2023.10097092
Appears in Collections:	[資訊科學系] 會議論文

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