title: Denoising as Adaptation: Noise-Space Domain Adaptation for Image
    Restoration
summary: 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.
paper_authors._id: 67978a6cba1b09be7b538b0a
paper_authors.name: Kang Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T14:09:50.240Z
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paper_authors.avatarUrl: /avatars/70f6fec5bf29c89eda7b909ec1472ace.svg
paper_authors.fullname: Kang Liao
paper_authors.type: user
paper_authors.user: KangLiao
paper_authors._id: 67978a6cba1b09be7b538b0b
paper_authors.name: Zongsheng Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-27T15:10:20.782Z
paper_authors._id: 630ad0dd2ff113e0fb31c6b0
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paper_authors.fullname: Zongsheng Yue
paper_authors.type: user
paper_authors.user: OAOA
paper_authors._id: 67978a6cba1b09be7b538b0c
paper_authors.name: Zhouxia Wang
paper_authors.status: admin_assigned
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paper_authors._id: 64db92a5858f8a41c11669b7
paper_authors.avatarUrl: /avatars/e834d8f1d4781e3bb0b5d6d25b3b3505.svg
paper_authors.fullname: Zhouxia Wang
paper_authors.type: user
paper_authors.user: wzhouxiff
paper_authors._id: 67978a6cba1b09be7b538b0d
paper_authors.name: Chen Change Loy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-27T15:10:07.079Z
paper_authors._id: 67459d997a49660f7f62452f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
paper_authors.fullname: Chen Change Loy
paper_authors.type: user
paper_authors.user: cavanloy
paper_title: Denoising as Adaptation: Noise-Space Domain Adaptation for Image
    Restoration
paper_summary: 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.
paper_ai_summary: Domain adaptation in image restoration is achieved through noise space using diffusion models to align synthetic and real-world outputs, utilizing strategies like channel-shuffling and contrastive learning.
paper_ai_keywords: diffusion models noise space denoising as adaptation channel-shuffling layer residual-swapping contrastive learning denoising deblurring deraining