Diffusion Probabilistic Models (DPMs) have recently shown remarkable performance in image generation tasks, which are capable of generating highly realistic images. When adopting DPMs for image restoration tasks, the crucial aspect lies in how to integrate the conditional information to guide the DPMs to generate accurate and natural output, which has been largely overlooked in existing works. In this paper, we present a unified conditional framework based on diffusion models for image restoration. We leverage a lightweight UNet to predict initial guidance and the diffusion model to learn the residual of the guidance. By carefully designing the basic module and integration module for the diffusion model block, we integrate the guidance and other auxiliary conditional information into every block of the diffusion model to achieve spatially-adaptive generation conditioning. To handle high-resolution images, we propose a simple yet effective inter-step patch-splitting strategy to produce arbitrary-resolution images without grid artifacts. We evaluate our conditional framework on three challenging tasks: extreme low-light denoising, deblurring, and JPEG restoration, demonstrating its significant improvements in perceptual quality and the generalization to restoration tasks.

翻译：扩散概率模型（DPMs）近年来在图像生成任务中展现出卓越性能，能够生成高度逼真的图像。将DPMs应用于图像恢复任务时，关键在于如何整合条件信息以引导DPMs生成准确且自然的输出，而现有研究对此问题关注不足。本文提出一种基于扩散模型的统一条件框架用于图像恢复。我们利用轻量级UNet预测初始引导信息，并通过扩散模型学习引导残差。通过精心设计扩散模型模块的基础模块与集成模块，我们将引导信息及其他辅助条件信息融入扩散模型的每个模块中，实现空间自适应生成条件控制。为处理高分辨率图像，我们提出一种简单有效的跨步幅块分裂策略，可在无网格伪影条件下生成任意分辨率图像。我们在三个具有挑战性的任务上评估所提条件框架：极低光照去噪、去模糊及JPEG恢复，实验表明该框架在感知质量与恢复任务泛化性方面均取得显著提升。