Although deep 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 the multi-step denoising process is
influenced by auxiliary conditional inputs, we obtain meaningful gradients from
noise prediction to gradually align the restored results of both synthetic and
real-world data to a common clean distribution. We refer to this method as
denoising as adaptation. To prevent shortcuts during training, we present
useful techniques such as channel shuffling and residual-swapping contrastive
learning. Experimental results on three classical image restoration tasks,
namely denoising, deblurring, and deraining, demonstrate the effectiveness of
the proposed method. Code will be released at:
this https URL

通过扩散模型在噪声空间中进行域适应，我们展示了一种针对深度学习图像修复的方法，其通过利用多步去噪过程受辅助条件输入影响的独特属性，逐渐将合成数据和真实世界数据的修复结果对齐到一个共同的干净分布。

去噪作为适应性问题：图像恢复的噪声空间域自适应

Denoising as Adaptation: Noise-Space Domain Adaptation for Image  Restoration

Deep learning-based image restoration methods have achieved promising
performance. However, how to faithfully preserve the structure of the original
image remains challenging. To address this challenge, we propose a novel
Residual-Conditioned Optimal Transport (RCOT) approach, which models the image
restoration as an optimal transport (OT) problem for both unpaired and paired
settings, integrating the transport residual as a unique degradation-specific
cue for both the transport cost and the transport map. Specifically, we first
formalize a Fourier residual-guided OT objective by incorporating the
degradation-specific information of the residual into the transport cost. Based
on the dual form of the OT formulation, we design the transport map as a
two-pass RCOT map that comprises a base model and a refinement process, in
which the transport residual is computed by the base model in the first pass
and then encoded as a degradation-specific embedding to condition the
second-pass restoration. By duality, the RCOT problem is transformed into a
minimax optimization problem, which can be solved by adversarially training
neural networks. Extensive experiments on multiple restoration tasks show the
effectiveness of our approach in terms of both distortion measures and
perceptual quality. Particularly, RCOT restores images with more faithful
structural details compared to state-of-the-art methods.

深度学习图像修复中，我们提出了一种新的残余条件的最优传输（RCOT）方法，该方法将图像修复建模为一种最优传输问题，通过整合传输残差作为独特的特定退化线索来保留原始图像的结构细节，并通过对抗训练神经网络来解决。与现有方法相比，RCOT 方法在多个修复任务中显示出更好的失真度量和感知质量，可以更忠实地恢复图像的结构细节。