1. NCCU Academic Hub
  2. Publications
  3. College of Informatics
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

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.
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.
Computer Vision and Pattern Recognition, 2017.
[21] Khan Muhammad, Jamil Ahmad, Zhihan Lv, Paolo Bellavista, Po Yang, and
Sung Wook Baik, “Efficient deep cnn-based fire detection and localization in video
surveillance applications,” IEEE Transactions on Systems, Man, and Cybernetics:
Systems, 2018.
43
[22] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao, “Deepdriving:
Learning affordance for direct perception in autonomous driving,” in Proc. Int’l
Conf. Computer Vision, 2015.
[23] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang, “Joint convolutional
analysis and synthesis sparse representation for single image layer separation,” in
Proc. Int’l Conf. Computer Vision, 2017.
[24] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based
rain streaks removal via image decomposition,” IEEE Trans. on Image Processing,
2011.
[25] Yi-Lei Chen and Chiou-Ting Hsu, “A generalized low-rank appearance model for
spatio-temporally correlated rain streaks,” in Proc. Int’l Conf. Computer Vision,
2013.
[26] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie,
Fu Lee Wang, and Meng Wang, “Detail-recovery image deraining via context aggregation
networks,” in Proc. Conf. Computer Vision and Pattern Recognition, 2020.
[27] Siyuan Li, Wenqi Ren, Jiawan Zhang, Jinke Yu, and Xiaojie Guo, “Single image
rain removal via a deep decomposition–composition network,” Computer Vision
and Image Understanding, 2019.
[28] Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang, “Learning deep cnn denoiser
prior for image restoration,” in Proc. Conf. Computer Vision and Pattern
Recognition, 2017.
[29] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha, “Recurrent
squeeze-and-excitation context aggregation net for single image deraining,” in Proceedings
of the European conference on computer vision (ECCV), 2018.
44
[30] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng, “Progressive
image deraining networks: A better and simpler baseline,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2019.
[31] Bo Pang, Deming Zhai, Junjun Jiang, and Xianming Liu, “Single image deraining
via scale-space invariant attention neural network,” in Proceedings of the 28th ACM
International Conference on Multimedia, 2020.
[32] Rajeev Yasarla and Vishal M Patel, “Uncertainty guided multi-scale residual
learning-using a cycle spinning cnn for single image de-raining,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2019.
[33] Hongyuan Zhu, Xi Peng, Joey Tianyi Zhou, Songfan Yang, Vijay Chanderasekh,
Liyuan Li, and Joo-Hwee Lim, “Singe image rain removal with unpaired information:
A differentiable programming perspective,” in Proc. Nat’l Conf. Artificial
Intelligence, 2019.
[34] Changfeng Yu, Yi Chang, Yi Li, Xile Zhao, and Luxin Yan, “Unsupervised image
deraining: Optimization model driven deep cnn,” in Proceedings of the 29th ACM
International Conference on Multimedia, 2021.
[35] Xin Jin, Zhibo Chen, Jianxin Lin, Zhikai Chen, and Wei Zhou, “Unsupervised single
image deraining with self-supervised constraints,” in Proc. Int’l Conf. Image
Processing. IEEE, 2019.
[36] Yanyan Wei, Zhao Zhang, Yang Wang, Mingliang Xu, Yi Yang, Shuicheng Yan, and
Meng Wang, “Deraincyclegan: Rain attentive cyclegan for single image deraining
and rainmaking,” IEEE Trans. on Image Processing, 2021.
[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros, “Unpaired image-to-
45
image translation using cycle-consistent adversarial networks,” in Proc. Int’l Conf.
Computer Vision, 2017.
[38] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu, “Semi-supervised
transfer learning for image rain removal,” in Proc. Conf. Computer Vision and Pattern
Recognition, 2019.
[39] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel, “Syn2real transfer learning
for image deraining using gaussian processes,” in Proc. Conf. Computer Vision
and Pattern Recognition, 2020.
[40] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan, “Closing the loop: Joint rain
generation and removal via disentangled image translation,” in Proc. Conf. Computer
Vision and Pattern Recognition, 2021.
[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N
Gomez, Łukasz Kaiser, and Illia Polosukhin, “Attention is all you need,” Proc.
Neural Information Processing Systems, 2017.
[42] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua
Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg
Heigold, Sylvain Gelly, et al., “An image is worth 16x16 words: Transformers
for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
[43] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander
Kirillov, and Sergey Zagoruyko, “End-to-end object detection with transformers,”
in Proc. Euro. Conf. Computer Vision. Springer, 2020.
[44] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin,
and Baining Guo, “Swin transformer: Hierarchical vision transformer using shifted
windows,” in Proc. Int’l Conf. Computer Vision, 2021.
46
[45] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and
Houqiang Li, “Uformer: A general u-shaped transformer for image restoration,” in
Proc. Conf. Computer Vision and Pattern Recognition, 2022.
[46] Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang
Zhou, Houqiang Li, and Qi Tian, “Tape: Task-agnostic prior embedding for image
restoration,” arXiv preprint arXiv:2203.06074, 2022.
[47] Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng, “All-inone
image restoration for unknown corruption,” in Proc. Conf. Computer Vision and
Pattern Recognition, 2022.
[48] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M Patel, “Transweather:
Transformer-based restoration of images degraded by adverse weather conditions,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2022.
[49] Yancheng Wang, Ning Xu, Chong Chen, and Yingzhen Yang, “Adaptive cross-layer
attention for image restoration,” arXiv preprint arXiv:2203.03619, 2022.
[50] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and
Ming-Hsuan Yang, “Burst image restoration and enhancement,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2022.
[51] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu, “Single
image deraining: From model-based to data-driven and beyond,” IEEE Trans. on
Pattern Analysis and Machine Intelligence, 2020.
[52] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based
rain streaks removal via image decomposition,” IEEE Trans. on Image Processing,
2011.
47
[53] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim, “Video deraining and desnowing
using temporal correlation and low-rank matrix completion,” IEEE Trans. on
Image Processing, 2015.
[54] Yu Luo, Yong Xu, and Hui Ji, “Removing rain from a single image via discriminative
sparse coding,” in Proc. Int’l Conf. Computer Vision, 2015.
[55] Kewen Han and Xinguang Xiang, “Decomposed cyclegan for single image deraining
with unpaired data,” in Proc. Int’l Conf. Acoustics, Speech, and Signal Processing.
IEEE, 2020.
[56] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley, “Clearing
the skies: A deep network architecture for single-image rain removal,” IEEE
Trans. on Image Processing, 2017.
[57] Jie Hu, Li Shen, and Gang Sun, “Squeeze-and-excitation networks, 7132–7141,”
in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt
Lake City, UT, 2018.
[58] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau,
“Spatial attentive single-image deraining with a high quality real rain dataset,” in
Proc. Conf. Computer Vision and Pattern Recognition, 2019.
[59] Dongwei Ren, Wei Shang, Pengfei Zhu, Qinghua Hu, Deyu Meng, and Wangmeng
Zuo, “Single image deraining using bilateral recurrent network,” IEEE Trans. on
Image Processing, 2020.
[60] Chenghao Chen and Hao Li, “Robust representation learning with feedback for
single image deraining,” in Proc. Conf. Computer Vision and Pattern Recognition,
2021.
48
[61] Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, and Chengpeng Chen, “Hinet: Half
instance normalization network for image restoration,” in Proc. Conf. Computer
Vision and Pattern Recognition, 2021.
[62] Kuldeep Purohit, Maitreya Suin, AN Rajagopalan, and Vishnu Naresh Boddeti,
“Spatially-adaptive image restoration using distortion-guided networks,” in Proc.
Int’l Conf. Computer Vision, 2021.
[63] Yizhou Li, Yusuke Monno, and Masatoshi Okutomi, “Single image deraining network
with rain embedding consistency and layered lstm,” in Proc. of the IEEE/CVF
Winter Conf. on Applications of Computer Vision, 2022.
[64] Pengpeng Li, Jiyu Jin, Guiyue Jin, Lei Fan, Xiao Gao, Tianyu Song, and Xiang
Chen, “Deep scale-space mining network for single image deraining,” in Proc.
Conf. Computer Vision and Pattern Recognition, 2022.
[65] Yuuto Nanba, Hikaru Miyata, and Xian-Hua Han, “Dual heterogeneous complementary
networks for single image deraining,” in Proc. Conf. Computer Vision and
Pattern Recognition, 2022.
[66] Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang, “Selective kernel networks,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2019.
[67] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory
Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., “Pytorch:
An imperative style, high-performance deep learning library,” Proc. Neural
Information Processing Systems, 2019.
[68] Diederik P Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,”
arXiv preprint arXiv:1412.6980, 2014.
49
[69] Quan Huynh-Thu and Mohammed Ghanbari, “Scope of validity of psnr in image/
video quality assessment,” Electronics letters, 2008.
[70] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment:
from error visibility to structural similarity,” IEEE Trans. on Image Processing,
2004.
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.
Computer Vision and Pattern Recognition, 2017.
[21] Khan Muhammad, Jamil Ahmad, Zhihan Lv, Paolo Bellavista, Po Yang, and
Sung Wook Baik, “Efficient deep cnn-based fire detection and localization in video
surveillance applications,” IEEE Transactions on Systems, Man, and Cybernetics:
Systems, 2018.
43
[22] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao, “Deepdriving:
Learning affordance for direct perception in autonomous driving,” in Proc. Int’l
Conf. Computer Vision, 2015.
[23] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang, “Joint convolutional
analysis and synthesis sparse representation for single image layer separation,” in
Proc. Int’l Conf. Computer Vision, 2017.
[24] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based
rain streaks removal via image decomposition,” IEEE Trans. on Image Processing,
2011.
[25] Yi-Lei Chen and Chiou-Ting Hsu, “A generalized low-rank appearance model for
spatio-temporally correlated rain streaks,” in Proc. Int’l Conf. Computer Vision,
2013.
[26] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie,
Fu Lee Wang, and Meng Wang, “Detail-recovery image deraining via context aggregation
networks,” in Proc. Conf. Computer Vision and Pattern Recognition, 2020.
[27] Siyuan Li, Wenqi Ren, Jiawan Zhang, Jinke Yu, and Xiaojie Guo, “Single image
rain removal via a deep decomposition–composition network,” Computer Vision
and Image Understanding, 2019.
[28] Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang, “Learning deep cnn denoiser
prior for image restoration,” in Proc. Conf. Computer Vision and Pattern
Recognition, 2017.
[29] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha, “Recurrent
squeeze-and-excitation context aggregation net for single image deraining,” in Proceedings
of the European conference on computer vision (ECCV), 2018.
44
[30] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng, “Progressive
image deraining networks: A better and simpler baseline,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2019.
[31] Bo Pang, Deming Zhai, Junjun Jiang, and Xianming Liu, “Single image deraining
via scale-space invariant attention neural network,” in Proceedings of the 28th ACM
International Conference on Multimedia, 2020.
[32] Rajeev Yasarla and Vishal M Patel, “Uncertainty guided multi-scale residual
learning-using a cycle spinning cnn for single image de-raining,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2019.
[33] Hongyuan Zhu, Xi Peng, Joey Tianyi Zhou, Songfan Yang, Vijay Chanderasekh,
Liyuan Li, and Joo-Hwee Lim, “Singe image rain removal with unpaired information:
A differentiable programming perspective,” in Proc. Nat’l Conf. Artificial
Intelligence, 2019.
[34] Changfeng Yu, Yi Chang, Yi Li, Xile Zhao, and Luxin Yan, “Unsupervised image
deraining: Optimization model driven deep cnn,” in Proceedings of the 29th ACM
International Conference on Multimedia, 2021.
[35] Xin Jin, Zhibo Chen, Jianxin Lin, Zhikai Chen, and Wei Zhou, “Unsupervised single
image deraining with self-supervised constraints,” in Proc. Int’l Conf. Image
Processing. IEEE, 2019.
[36] Yanyan Wei, Zhao Zhang, Yang Wang, Mingliang Xu, Yi Yang, Shuicheng Yan, and
Meng Wang, “Deraincyclegan: Rain attentive cyclegan for single image deraining
and rainmaking,” IEEE Trans. on Image Processing, 2021.
[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros, “Unpaired image-to-
45
image translation using cycle-consistent adversarial networks,” in Proc. Int’l Conf.
Computer Vision, 2017.
[38] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu, “Semi-supervised
transfer learning for image rain removal,” in Proc. Conf. Computer Vision and Pattern
Recognition, 2019.
[39] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel, “Syn2real transfer learning
for image deraining using gaussian processes,” in Proc. Conf. Computer Vision
and Pattern Recognition, 2020.
[40] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan, “Closing the loop: Joint rain
generation and removal via disentangled image translation,” in Proc. Conf. Computer
Vision and Pattern Recognition, 2021.
[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N
Gomez, Łukasz Kaiser, and Illia Polosukhin, “Attention is all you need,” Proc.
Neural Information Processing Systems, 2017.
[42] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua
Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg
Heigold, Sylvain Gelly, et al., “An image is worth 16x16 words: Transformers
for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
[43] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander
Kirillov, and Sergey Zagoruyko, “End-to-end object detection with transformers,”
in Proc. Euro. Conf. Computer Vision. Springer, 2020.
[44] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin,
and Baining Guo, “Swin transformer: Hierarchical vision transformer using shifted
windows,” in Proc. Int’l Conf. Computer Vision, 2021.
46
[45] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and
Houqiang Li, “Uformer: A general u-shaped transformer for image restoration,” in
Proc. Conf. Computer Vision and Pattern Recognition, 2022.
[46] Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang
Zhou, Houqiang Li, and Qi Tian, “Tape: Task-agnostic prior embedding for image
restoration,” arXiv preprint arXiv:2203.06074, 2022.
[47] Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng, “All-inone
image restoration for unknown corruption,” in Proc. Conf. Computer Vision and
Pattern Recognition, 2022.
[48] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M Patel, “Transweather:
Transformer-based restoration of images degraded by adverse weather conditions,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2022.
[49] Yancheng Wang, Ning Xu, Chong Chen, and Yingzhen Yang, “Adaptive cross-layer
attention for image restoration,” arXiv preprint arXiv:2203.03619, 2022.
[50] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and
Ming-Hsuan Yang, “Burst image restoration and enhancement,” in Proc. Conf.
Computer Vision and Pattern Recognition, 2022.
[51] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu, “Single
image deraining: From model-based to data-driven and beyond,” IEEE Trans. on
Pattern Analysis and Machine Intelligence, 2020.
[52] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu, “Automatic single-image-based
rain streaks removal via image decomposition,” IEEE Trans. on Image Processing,
2011.
47
[53] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim, “Video deraining and desnowing
using temporal correlation and low-rank matrix completion,” IEEE Trans. on
Image Processing, 2015.
[54] Yu Luo, Yong Xu, and Hui Ji, “Removing rain from a single image via discriminative
sparse coding,” in Proc. Int’l Conf. Computer Vision, 2015.
[55] Kewen Han and Xinguang Xiang, “Decomposed cyclegan for single image deraining
with unpaired data,” in Proc. Int’l Conf. Acoustics, Speech, and Signal Processing.
IEEE, 2020.
[56] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley, “Clearing
the skies: A deep network architecture for single-image rain removal,” IEEE
Trans. on Image Processing, 2017.
[57] Jie Hu, Li Shen, and Gang Sun, “Squeeze-and-excitation networks, 7132–7141,”
in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt
Lake City, UT, 2018.
[58] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau,
“Spatial attentive single-image deraining with a high quality real rain dataset,” in
Proc. Conf. Computer Vision and Pattern Recognition, 2019.
[59] Dongwei Ren, Wei Shang, Pengfei Zhu, Qinghua Hu, Deyu Meng, and Wangmeng
Zuo, “Single image deraining using bilateral recurrent network,” IEEE Trans. on
Image Processing, 2020.
[60] Chenghao Chen and Hao Li, “Robust representation learning with feedback for
single image deraining,” in Proc. Conf. Computer Vision and Pattern Recognition,
2021.
48
[61] Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, and Chengpeng Chen, “Hinet: Half
instance normalization network for image restoration,” in Proc. Conf. Computer
Vision and Pattern Recognition, 2021.
[62] Kuldeep Purohit, Maitreya Suin, AN Rajagopalan, and Vishnu Naresh Boddeti,
“Spatially-adaptive image restoration using distortion-guided networks,” in Proc.
Int’l Conf. Computer Vision, 2021.
[63] Yizhou Li, Yusuke Monno, and Masatoshi Okutomi, “Single image deraining network
with rain embedding consistency and layered lstm,” in Proc. of the IEEE/CVF
Winter Conf. on Applications of Computer Vision, 2022.
[64] Pengpeng Li, Jiyu Jin, Guiyue Jin, Lei Fan, Xiao Gao, Tianyu Song, and Xiang
Chen, “Deep scale-space mining network for single image deraining,” in Proc.
Conf. Computer Vision and Pattern Recognition, 2022.
[65] Yuuto Nanba, Hikaru Miyata, and Xian-Hua Han, “Dual heterogeneous complementary
networks for single image deraining,” in Proc. Conf. Computer Vision and
Pattern Recognition, 2022.
[66] Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang, “Selective kernel networks,”
in Proc. Conf. Computer Vision and Pattern Recognition, 2019.
[67] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory
Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., “Pytorch:
An imperative style, high-performance deep learning library,” Proc. Neural
Information Processing Systems, 2019.
[68] Diederik P Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,”
arXiv preprint arXiv:1412.6980, 2014.
49
[69] Quan Huynh-Thu and Mohammed Ghanbari, “Scope of validity of psnr in image/
video quality assessment,” Electronics letters, 2008.
[70] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment:
from error visibility to structural similarity,” IEEE Trans. on Image Processing,
2004.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202201736en_US