UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration
This work addresses the challenge of restoring images with diverse and compound degradations, which is incremental as it builds on existing diffusion prior methods.
The authors tackled the problem of All-in-One Image Restoration by proposing UniLDiff, a unified framework that uses diffusion priors with degradation- and detail-aware mechanisms, achieving state-of-the-art performance in multi-task and mixed degradation settings.
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.