| dc.contributor | 資訊系 | |
| dc.creator (作者) | 彭彥璁 | |
| dc.creator (作者) | Peng, Yan-Tsung;Li, Wei-Hua | |
| dc.date (日期) | 2023-06 | |
| dc.date.accessioned | 16-Feb-2024 15:36:56 (UTC+8) | - |
| dc.date.available | 16-Feb-2024 15:36:56 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-Feb-2024 15:36:56 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/149884 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 113 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE | |
| dc.subject (關鍵詞) | Image deraining; self-supervised; stochastic derained references | |
| dc.title (題名) | Rain2Avoid: Self-Supervised Single Image Deraining | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/ICASSP49357.2023.10097092 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/ICASSP49357.2023.10097092 | |
