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.
View on arXiv@article{jiang2025_2503.17915,
title={ Cat-AIR: Content and Task-Aware All-in-One Image Restoration },
author={ Jiachen Jiang and Tianyu Ding and Ke Zhang and Jinxin Zhou and Tianyi Chen and Ilya Zharkov and Zhihui Zhu and Luming Liang },
journal={arXiv preprint arXiv:2503.17915},
year={ 2025 }
}