Efficient Degradation-agnostic Image Restoration via Channel-Wise Functional Decomposition and Manifold Regularization

Understand

Degradation-agnostic image restoration aims to handle diverse corruptions with one unified model, but faces fundamental challenges in balancing efficiency and performance across different degradation types.

• Existing approaches either sacrifice efficiency for versatility or fail to capture the distinct representational requirements of various degradations.
• We present MIRAGE, an efficient framework that addresses these challenges through two key innovations.
• First, we propose a channel-wise functional decomposition that systematically repurposes channel redundancy in attention mechanisms by assigning CNN, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively.