CoA: Towards Real Image Dehazing via Compression-and-Adaptation
Long Ma, Yuxin Feng, Yan Zhang, Jinyuan Liu, Weimin Wang, Guang-Yong Chen, Chengpei Xu, Zhuo Su
Abstract
Figure 1. Performance evaluation. The proposed CoA incorporates model compression in synthetic domain for efficiency and bilevel adaptation in real domain for adaptability, as illustrated in the central sub-figure (b). The left sub-figure (a) presents efficiency across various metrics, clearly showing that our CoA outperforms others by a significant margin. The right sub-figure (c) shows adaptability across different scenes, where it is evident that our CoA consistently performs excellently.
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Install the CLIlune papers fulltext 824694f1-a455-41fd-9dba-d6433a53f2aaCited by top-tier papers2
- Bilevel Layer-Positioning LoRA for Real Image DehazingYan Zhang, Long Ma, Yuxin Feng, Zhe Huang et al.CVPR 2026 · 13 citations
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Builds on16
- Toward Fast, Flexible, and Robust Low-Light Image EnhancementLong Ma, Tengyu Ma, Risheng Liu, Xin Fan et al.CVPR 2022 · 928 citations
- Image Dehazing Transformer with Transmission-Aware 3D Position EmbeddingChunle Guo, Qixin Yan, Saeed Anwar, Runmin Cong et al.CVPR 2022 · 464 citations
- All-In-One Image Restoration for Unknown CorruptionBoyun Li, Xiao Liu, Peng Hu, Zhongqin Wu et al.CVPR 2022 · 338 citations
- From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real DataYe Liu, Lei Zhu, Shunda Pei, Huazhu Fu et al.ACM MM 2021 · 197 citations
- Nighttime Dehazing with a Synthetic BenchmarkJing Zhang, Yang Cao, Zheng-Jun Zha, Dacheng TaoACM MM 2020 · 137 citations
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