Cat-AIR: Content and Task-Aware All-in-One Image Restoration
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916660377550848 |
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| author | Jiang, Jiachen Ding, Tianyu Zhang, Ke Zhou, Jinxin Chen, Tianyi Zharkov, Ilya Zhu, Zhihui Liang, Luming |
| author_facet | Jiang, Jiachen Ding, Tianyu Zhang, Ke Zhou, Jinxin Chen, Tianyi Zharkov, Ilya Zhu, Zhihui Liang, Luming |
| contents | All-in-one image restoration seeks to recover high-quality images from various types of degradation using a single model, without prior knowledge of the corruption source. However, existing methods often struggle to effectively and efficiently handle multiple degradation types. We present Cat-AIR, a novel \textbf{C}ontent \textbf{A}nd \textbf{T}ask-aware framework for \textbf{A}ll-in-one \textbf{I}mage \textbf{R}estoration. Cat-AIR incorporates an alternating spatial-channel attention mechanism that adaptively balances the local and global information for different tasks. Specifically, we introduce cross-layer channel attentions and cross-feature spatial attentions that allocate computations based on content and task complexity. Furthermore, we propose a smooth learning strategy that allows for seamless adaptation to new restoration tasks while maintaining performance on existing ones. Extensive experiments demonstrate that Cat-AIR achieves state-of-the-art results across a wide range of restoration tasks, requiring fewer FLOPs than previous methods, establishing new benchmarks for efficient all-in-one image restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cat-AIR: Content and Task-Aware All-in-One Image Restoration Jiang, Jiachen Ding, Tianyu Zhang, Ke Zhou, Jinxin Chen, Tianyi Zharkov, Ilya Zhu, Zhihui Liang, Luming Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning All-in-one image restoration seeks to recover high-quality images from various types of degradation using a single model, without prior knowledge of the corruption source. However, existing methods often struggle to effectively and efficiently handle multiple degradation types. We present Cat-AIR, a novel \textbf{C}ontent \textbf{A}nd \textbf{T}ask-aware framework for \textbf{A}ll-in-one \textbf{I}mage \textbf{R}estoration. Cat-AIR incorporates an alternating spatial-channel attention mechanism that adaptively balances the local and global information for different tasks. Specifically, we introduce cross-layer channel attentions and cross-feature spatial attentions that allocate computations based on content and task complexity. Furthermore, we propose a smooth learning strategy that allows for seamless adaptation to new restoration tasks while maintaining performance on existing ones. Extensive experiments demonstrate that Cat-AIR achieves state-of-the-art results across a wide range of restoration tasks, requiring fewer FLOPs than previous methods, establishing new benchmarks for efficient all-in-one image restoration. |
| title | Cat-AIR: Content and Task-Aware All-in-One Image Restoration |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.17915 |