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.

本文提出了一种基于扩散模型的统一条件框架，结合了轻量级 UNet 和固有的差异模型来增强图像恢复的物理约束，采用基本和集成模块实现了对所有块的引导和其他辅助条件信息的整合，并提出了简单而有效的截距补丁分裂策略，将其用于低光量去噪、去模糊和 JPEG 图像恢复的挑战任务中，得到了显著的感知质量提升和恢复任务的推广。