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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 5-Jan-2023 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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Subramanian, and CV Jawahar, “Fluid: Few-shot self-supervised image
deraining,” in Proc. of the IEEE/CVF Winter Conf. on Applications of Computer
Vision, 2022.
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deraining with rain direction regularizer,” in Proc. Int’l Conf. Computer Vision, 2021.
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41
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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
dc.contributor.advisor 彭彥璁zh_TW
dc.contributor.advisor Peng, Yan-Tsungen_US
dc.contributor.author (Authors) 彭文藝zh_TW
dc.contributor.author (Authors) Peng, Wen-Yien_US
dc.creator (作者) 彭文藝zh_TW
dc.creator (作者) Peng, Wen-Yien_US
dc.date (日期) 2022en_US
dc.date.accessioned 5-Jan-2023 15:18:57 (UTC+8)-
dc.date.available 5-Jan-2023 15:18:57 (UTC+8)-
dc.date.issued (上傳時間) 5-Jan-2023 15:18:57 (UTC+8)-
dc.identifier (Other Identifiers) G0109753113en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142893-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 109753113zh_TW
dc.description.abstract (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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents Abstract i
Contents ii
List of Figures iv
List of Tables vii
1 Introduction 1
1.1 Motivation and Challenges 1
1.2 Thesis Structure 4
2 Related Work 5
2.1 Conditional Image Processing Methods 6
2.2 Deep Learning-based Methods 6
2.2.1 Unsupervised Methods 6
2.2.2 Semi-supervised Methods 8
2.2.3 Supervised Methods 8
3 Proposed Method 14
3.1 Network Architecture 14
3.1.1 Feature Extraction term with CSP-M 16
3.1.2 Global and Local Rain-aware Attention (GLRA) 17
3.1.3 Atrous Convolution MLP (ACMLP) 20
3.2 Loss Function 21
4 Experimental Results 23
4.1 Implementation Settings 23
4.2 Quantitative Analysis 28
4.3 Qualitative Analysis 30
4.4 Ablation Study 34
5 Conclusions 40
References 41		zh_TW
dc.format.extent 12646823 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109753113en_US
dc.subject (關鍵詞) 除雨zh_TW
dc.subject (關鍵詞) 單一影像除雨zh_TW
dc.subject (關鍵詞) 監督式zh_TW
dc.subject (關鍵詞) Single image derainingen_US
dc.subject (關鍵詞) Superviseden_US
dc.subject (關鍵詞) Derainingen_US
dc.subject (關鍵詞) Transformeren_US
dc.title (題名) 用於高效影像除雨之多階段分區轉換器zh_TW
dc.title (題名) Multi-Stage Partitioned Transformer for Efficient Image Derainingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, and Michael S Brown, “Rain streak
removal using layer priors,” in Proc. Conf. Computer Vision and Pattern Recognition,
2016.
[2] Yi Chang, Luxin Yan, and Sheng Zhong, “Transformed low-rank model for line
pattern noise removal,” in Proc. Int’l Conf. Computer Vision, 2017.
[3] Shyam Nandan Rai, Rohit Saluja, Chetan Arora, Vineeth N Balasubramanian, Anbumani
Subramanian, and CV Jawahar, “Fluid: Few-shot self-supervised image
deraining,” in Proc. of the IEEE/CVF Winter Conf. on Applications of Computer
Vision, 2022.
[4] Yang Liu, Ziyu Yue, Jinshan Pan, and Zhixun Su, “Unpaired learning for deep image
deraining with rain direction regularizer,” in Proc. Int’l Conf. Computer Vision, 2021.
[5] Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and
Shuicheng Yan, “Deep joint rain detection and removal from a single image,” in
Proc. Conf. Computer Vision and Pattern Recognition, 2017.
[6] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi
Ma, and Junjun Jiang, “Multi-scale progressive fusion network for single image
deraining,” in Proc. Conf. Computer Vision and Pattern Recognition, 2020.
41
[7] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng, “A model-driven deep neural
network for single image rain removal,” in Proc. Conf. Computer Vision and Pattern
Recognition, 2020.
[8] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz
Khan, Ming-Hsuan Yang, and Ling Shao, “Multi-stage progressive image restoration,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2021.
[9] Yuanchu Liang, Saeed Anwar, and Yang Liu, “Drt: A lightweight single image deraining
recursive transformer,” in Proc. Conf. Computer Vision and Pattern Recognition,
2022.
[10] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz
Khan, and Ming-Hsuan Yang, “Restormer: Efficient transformer for high-resolution
image restoration,” in Proc. Conf. Computer Vision and Pattern Recognition, 2022.
[11] Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, and Dongbo Min, “Knn local attention
for image restoration,” in Proc. Conf. Computer Vision and Pattern Recognition,
2022.
[12] Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei
Hsieh, and I-Hau Yeh, “Cspnet: A new backbone that can enhance learning capability
of cnn,” in Proc. Conf. Computer Vision and Pattern Recognition, 2020.
[13] Wenhan Yang, Robby T Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying
Liu, “Joint rain detection and removal from a single image with contextualized
deep networks,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 2019.
[14] He Zhang, Vishwanath Sindagi, and Vishal M Patel, “Image de-raining using a conditional
generative adversarial network,” IEEE transactions on circuits and systems
for video technology, 2019.
42
[15] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley,
“Removing rain from single images via a deep detail network,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2017.
[16] He Zhang and Vishal M Patel, “Density-aware single image de-raining using a multistream
dense network,” in Proc. Conf. Computer Vision and Pattern Recognition,
2018.
[17] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H.
Lau, “Spatial attentive single-image deraining with a high quality real rain dataset,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2019.
[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua
Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg
Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby, “An image is worth
16x16 words: Transformers for image recognition at scale,” in Proc. Int’l Conf.
Learning Representations, 2021.
[19] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu,
Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao, “Pre-trained image processing
transformer,” in Proc. Conf. Computer Vision and Pattern Recognition, 2021.
[20] Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and
Serge Belongie, “Feature pyramid networks for object detection,” in Proc. Conf.
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dc.identifier.doi (DOI) 10.6814/NCCU202201736en_US