Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts

ArXi:2605.11444v1 Announce Type: new All-in-one image restoration seeks to recover clean images from inputs affected by diverse and unknown degradations using a unified framework. Recent methods have shown strong performance by identifying degradation characteristics to guide the restoration process. However, many of them treat degradations as discrete categories, which limits their ability to model the continuous relational structure that arises in composite degradations.