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CAN: Cascade Augmentations Against Noise for Image Restoration

文献类型：期刊论文

作者Yan, Yanyang1,2; Yao, Siyuan3; Ren, Wenqi4; Zhang, Rui5; Guo, Qi5; Cao, Xiaochun4
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2025
卷号34页码:5131-5146
关键词Image restoration cascade augmentations cascade augmentations noise corruptions noise corruptions
ISSN号1057-7149
DOI10.1109/TIP.2025.3595374
英文摘要Image restoration aims to recover the latent clean image from a degraded counterpart. In general, the prevailing state-of-the-art image restoration methods concentrate on solving only a specific degradation type according to the task, e.g., deblurring or deraining. However, if the corresponding well-trained frameworks confront other real-world image corruptions, i.e., the corruptions are not covered in the training phase, and state-of-the-art restoration models will suffer from a lack of generalization ability. We have observed that an image restoration model can be easily confused by noise corruption. Towards improving the robustness of image restoration networks, in this paper, we focus on alleviating the corruption of noise in various image restoration tasks, which is almost inevitable in real-world scenes. To this end, we devise a novel Cascade Augmentation strategy against Noise (CAN) to enhance the robustness of specific image restoration. Specifically, the given degraded images are sequentially augmented from different perspectives, i.e., noise-aware augmentation and model-aware augmentation. The noise-aware augmentation is proposed to enrich the samples by introducing various noise operations. Moreover, to adapt to more unknown corruptions, we propose a novel model-aware augmentation mechanism, which enhances the scalability by exploring useful both spatial and frequency clues with the help of model randomness. It is worth noting that the proposed augmentation scheme is model-agnostic, and it can plug and play into arbitrary state-of-the-art image restoration architectures. In addition, we construct noise corruption benchmark datasets, derived from the validation set of standard image restoration datasets, to assist us in evaluating the robustness of restoration networks. Extensive quantitative and qualitative evaluations demonstrate that the proposed method has strong generalization capability, which can enhance the robustness of various image restoration frameworks when facing diverse noises.
资助项目National Natural Science Foundation of China[62302480] ; National Natural Science Foundation of China[62402055] ; National Natural Science Foundation of China[62322216] ; National Natural Science Foundation of China[62172409] ; National Natural Science Foundation of China[62311530686]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001554452200008
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/41780]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ren, Wenqi
作者单位1.Commun Univ China, Key Lab Media Audio & Video, Minist Educ, Beijing 100024, Peoples R China
2.Commun Univ China, Sch Data Sci & Media Intelligence, Beijing 100024, Peoples R China
3.Beijing Univ Posts & Telecommun, Sch Comp Sci, Nat Pilot Software Engn Sch, Beijing 100876, Peoples R China
4.Sun Yat Sen Univ, Sch Cyber Secur, Shenzhen 518107, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, State Key Lab Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Yanyang,Yao, Siyuan,Ren, Wenqi,et al. CAN: Cascade Augmentations Against Noise for Image Restoration[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2025,34:5131-5146.
APA Yan, Yanyang,Yao, Siyuan,Ren, Wenqi,Zhang, Rui,Guo, Qi,&Cao, Xiaochun.(2025).CAN: Cascade Augmentations Against Noise for Image Restoration.IEEE TRANSACTIONS ON IMAGE PROCESSING,34,5131-5146.
MLA Yan, Yanyang,et al."CAN: Cascade Augmentations Against Noise for Image Restoration".IEEE TRANSACTIONS ON IMAGE PROCESSING 34(2025):5131-5146.

入库方式： OAI收割

来源：计算技术研究所

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