LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling

• URL: http://arxiv.org/abs/2507.00790v2
• Date: Fri, 04 Jul 2025 20:39:37 GMT
• Title: LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
• Authors: Huaqiu Li, Yong Wang, Tongwen Huang, Hailang Huang, Haoqian Wang, Xiangxiang Chu,
• Abstract summary: We propose a dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model.<n>Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition.
• Score: 23.886038479359918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unified image restoration is a significantly challenging task in low-level vision. Existing methods either make tailored designs for specific tasks, limiting their generalizability across various types of degradation, or rely on training with paired datasets, thereby suffering from closed-set constraints. To address these issues, we propose a novel, dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model. Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition. Furthermore, it utilizes a lightweight module to align the degraded input with the generated preference of the diffusion model, and employs recurrent refinement for posterior sampling. Extensive experiments demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness and robustness. Our code and data will be available at https://github.com/AMAP-ML/LD-RPS.

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