标题： LD-RPS：通过潜在扩散递归后验采样实现零样本统一图像修复 显示英文标题

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

摘要： 统一图像修复是低层视觉中一个具有挑战性的任务。 现有方法要么为特定任务进行定制设计，限制了它们在各种退化类型中的泛化能力，要么依赖于成对数据集的训练，从而受到闭集约束。 为了解决这些问题，我们提出了一种新颖的、无需数据集的统一方法，通过使用预训练潜在扩散模型进行递归后验采样。 我们的方法结合多模态理解模型，在无任务条件下为生成模型提供语义先验。 此外，它利用一个轻量级模块将退化的输入与扩散模型的生成偏好对齐，并采用递归细化进行后验采样。 大量实验表明，我们的方法优于最先进方法，验证了其有效性和鲁棒性。 我们的代码和数据将在 https://github.com/AMAP-ML/LD-RPS 上提供。 显示英文摘要

摘要： 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.