Text Boosts Generalization: A Plug-and-Play Captioner for Real-World Image Restoration

Haoze Sun, Wenbo Li, Jiayue Liu, Kaiwen Zhou, Yongqiang Chen, Yong Guo, Yanwei Li, Renjing Pei, Long Peng, Yujiu Yang

Keywords: Image Restoration, Generative models, Diffusion Model, Multimodel Large Language Model

Abstract: Generalization has long been a central challenge in real-world image restoration. While recent diffusion-based restoration methods, which leverage generative priors from text-to-image models, have made progress in recovering more realistic details, they still encounter "generative capability inactivation" when applied to out-of-distribution data. To address this, we propose using text as an auxiliary invariant representation to reactivate the generative capabilities of these models. We begin by identifying two key properties of text input in diffusion-based restoration: richness and relevance, and examine their respective influence on model performance. Building on these insights, we introduce Res-Captioner, a module that generates enhanced textual descriptions tailored to image content and degradation levels, effectively mitigating response failures. Additionally, we present RealIR, a new benchmark designed to capture diverse real-world scenarios. Extensive experiments demonstrate that Res-Captioner significantly boosts the generalization ability of diffusion-based restoration models, while remaining fully plug-and-play.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 5942