Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data synthesis pipelines, estimating degradation kernels, employing deep internal learning, and performing domain adaptation and regularization. Previous domain adaptation methods have sought to bridge the domain gap by learning domain-invariant knowledge in either feature or pixel space. However, these techniques often struggle to extend to low-level vision tasks within a stable and compact framework. In this paper, we show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss that guides the restoration model in progressively aligning both restored synthetic and real-world outputs with a target clean distribution. We refer to this method as denoising as adaptation. To prevent shortcuts during joint training, we present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model. They implicitly blur the boundaries between conditioned synthetic and real data and prevent the reliance of the model on easily distinguishable features. Experimental results on three classical image restoration tasks, namely denoising, deblurring, and deraining, demonstrate the effectiveness of the proposed method.

翻译：尽管基于学习的图像复原方法已取得显著进展，但由于在合成数据上训练导致的显著域差异，其在实际场景中的泛化能力仍然受限。现有方法通过改进数据合成流程、估计退化核、采用深度内部学习以及执行域适应与正则化等手段应对此问题。以往的域适应方法试图通过在特征空间或像素空间学习域不变知识来弥合域差异，但这些技术往往难以在稳定紧凑的框架内扩展至低层视觉任务。本文提出一种基于扩散模型的噪声空间域适应方法。具体而言，通过利用辅助条件输入影响多步去噪过程的独特性质，我们推导出一种具有明确指导意义的扩散损失，该损失能够引导复原模型逐步将合成数据与真实数据的复原输出与目标清晰分布对齐。我们将此方法称为"去噪即适应"。为防止联合训练中出现捷径学习，我们在扩散模型中引入了通道混洗层与残差交换对比学习等关键策略。这些策略能够隐式模糊条件合成数据与真实数据之间的边界，防止模型依赖易于区分的特征。在去噪、去模糊和去雨三个经典图像复原任务上的实验结果验证了所提方法的有效性。