政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149884
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51599676      線上人數 : 947
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/149884


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/149884


    題名: Rain2Avoid: Self-Supervised Single Image Deraining
    作者: 彭彥璁
    Peng, Yan-Tsung;Li, Wei-Hua
    貢獻者: 資訊系
    關鍵詞: Image deraining;self-supervised;stochastic derained references
    日期: 2023-06
    上傳時間: 2024-02-16 15:36:56 (UTC+8)
    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.
    關聯: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE
    資料類型: conference
    DOI 連結: https://doi.org/10.1109/ICASSP49357.2023.10097092
    DOI: 10.1109/ICASSP49357.2023.10097092
    顯示於類別:[資訊科學系] 會議論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML148檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋