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arXiv · 2507.23685

UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration

Zihan Cheng·Liangtai Zhou·Dian Chen·Ni Tang·Xiaotong Luo·Yanyun Qu
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Abstract

All-in-One Image Restoration (AiOIR) has emerged as a promising yet challenging research direction. To address the core challenges of diverse degradation modeling and detail preservation, we propose UniLDiff, a unified framework enhanced with degradation- and detail-aware mechanisms, unlocking the power of diffusion priors for robust image restoration. Specifically, we introduce a Degradation-Aware Feature Fusion (DAFF) to dynamically inject low-quality features into each denoising step via decoupled fusion and adaptive modulation, enabling implicit modeling of diverse and compound degradations. Furthermore, we design a Detail-Aware Expert Module (DAEM) in the decoder to enhance texture and fine-structure recovery through expert routing. Extensive experiments across multi-task and mixed degradation settings demonstrate that our method consistently achieves state-of-the-art performance, highlighting the practical potential of diffusion priors for unified image restoration. Our code will be released.

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Zihan Cheng, Liangtai Zhou, Dian Chen, Ni Tang, Xiaotong Luo, Yanyun Qu. 2025-07-31. UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration. https://arxiv.org/abs/2507.23685

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