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

TL;DR

UniLDiff is a novel unified image restoration framework that leverages diffusion priors and specialized modules to effectively handle diverse degradations and preserve fine details, achieving state-of-the-art results.

Contribution

We introduce UniLDiff, a comprehensive framework with degradation- and detail-aware mechanisms, including DAFF and DAEM, to improve all-in-one image restoration performance.

Findings

Achieves state-of-the-art results across multiple restoration tasks.

Effectively models diverse and compound degradations.

Enhances texture and fine-structure recovery.

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.