Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts
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. To address this issue, we propose a multimodal large language model (MLLM)-guided image restoration framework that exploits multimodal embeddings as guidance for low-level restoration. Specifically, MLLM-derived features are injected into an encoder-decode...
| Authors | Eunho Lee, Rei Kawakami, Youngbae Hwang |
|---|---|
| Primary category | cs.CV |
| Published | 12 May 2026 |
| Last updated at source | 13 May 2026 |
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