政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142893

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142893

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/142893

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題名:	用於高效影像除雨之多階段分區轉換器 Multi-Stage Partitioned Transformer for Efficient Image Deraining
作者:	彭文藝 Peng, Wen-Yi
貢獻者:	彭彥璁 Peng, Yan-Tsung 彭文藝 Peng, Wen-Yi
關鍵詞:	除雨單一影像除雨監督式 Single image deraining Supervised Deraining Transformer
日期:	2022
上傳時間:	2023-01-05 15:18:57 (UTC+8)
摘要:	影像除雨是一種低階還原任務，在過去十幾年中變得非常熱門。影像除雨目的為恢復有雨的影像中的架構與細節紋理，並同時能處理各種影像大小與不同場景。儘管已存在許多強大的影像處雨模型，但大部分的研究方法都著重在建構更深更複雜的架構來訓練網路。因此，我們提出一個根據Transformer架構的除雨架構，並將網路切分成兩個部分，其中包含一個全域雨局部的雨水感知注意力模組;一個有效收集不同解析度下紋理資訊的MLP空洞卷機模組。透過廣泛的實驗與驗證，實驗結果顯示所提出方法之優越性。 Image deraining is a low-level restoration task that has become quite popular during the past decades. Its claims not only recover the spatial detail and high-level contextual structure of input rainy image but also need to deal with various resolutions or scenes. Although there exist a lot of robust deraining networks, the dominant researchers devote to constructing more deeper and complicated architecture to reconstruct the image texture. This work presents a transformer emulator which includes its own design Global and Local Rain-aware Attention (GLRA) and Atrous Convolution with MLP (ACMLP) for efficient exploring both detailed structure and semantic contexts. We validate performance via wide-ranging experiments, including synthetic and real world datasets showing the proposed method`s superiority.
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描述:	碩士國立政治大學資訊科學系 109753113
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109753113
資料類型:	thesis
DOI:	10.6814/NCCU202201736
顯示於類別:	[資訊科學系] 學位論文

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