Cat-AIR: Content and Task-Aware All-in-One Image Restoration
This addresses the challenge of efficiently restoring images from unknown corruptions for applications in computer vision, though it appears incremental as it builds on existing all-in-one frameworks.
The paper tackles the problem of all-in-one image restoration, where a single model handles various image degradations, by proposing Cat-AIR, which achieves state-of-the-art results across multiple tasks with fewer FLOPs than previous methods.
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.