Denoising as Adaptation: Noise-Space Domain Adaptation for Image
  Restoration
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.
67978a6cba1b09be7b538b0a
Kang Liao
claimed_verified
2025-02-25T14:09:50.240Z
65bc98383b879593a5a2f5e5
https://cdn-avatars.huggingface.co/v1/production/uploads/65bc98383b879593a5a2f5e5/p2ZtoTFN6tW-QkcPJf7YT.jpeg
Kang Liao
user
KangLiao
67978a6cba1b09be7b538b0b
Zongsheng Yue
admin_assigned
2025-01-27T15:10:20.782Z
630ad0dd2ff113e0fb31c6b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
Zongsheng Yue
user
OAOA
67978a6cba1b09be7b538b0c
Zhouxia Wang
admin_assigned
2025-01-27T15:10:14.521Z
64db92a5858f8a41c11669b7
/avatars/e834d8f1d4781e3bb0b5d6d25b3b3505.svg
Zhouxia Wang
user
wzhouxiff
67978a6cba1b09be7b538b0d
Chen Change Loy
admin_assigned
2025-01-27T15:10:07.079Z
67459d997a49660f7f62452f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
Chen Change Loy
user
cavanloy
Denoising as Adaptation: Noise-Space Domain Adaptation for Image
  Restoration
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.
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.
diffusion models noise space denoising as adaptation channel-shuffling layer residual-swapping contrastive learning denoising deblurring deraining