title: Restoration by Generation with Constrained Priors
summary: The inherent generative power of denoising diffusion models makes them
  well-suited for image restoration tasks where the objective is to find the
  optimal high-quality image within the generative space that closely resembles
  the input image. We propose a method to adapt a pretrained diffusion model for
  image restoration by simply adding noise to the input image to be restored and
  then denoise. Our method is based on the observation that the space of a
  generative model needs to be constrained. We impose this constraint by
  finetuning the generative model with a set of anchor images that capture the
  characteristics of the input image. With the constrained space, we can then
  leverage the sampling strategy used for generation to do image restoration. We
  evaluate against previous methods and show superior performances on multiple
  real-world restoration datasets in preserving identity and image quality. We
  also demonstrate an important and practical application on personalized
  restoration, where we use a personal album as the anchor images to constrain
  the generative space. This approach allows us to produce results that
  accurately preserve high-frequency details, which previous works are unable to
  do. Project webpage: https://gen2res.github.io.
paper_authors._id: 658e6454315340de5fe68764
paper_authors.name: Zheng Ding
paper_authors._id: 658e6454315340de5fe68765
paper_authors.name: Xuaner Zhang
paper_authors._id: 658e6454315340de5fe68766
paper_authors.name: Zhuowen Tu
paper_authors._id: 658e6454315340de5fe68767
paper_authors.name: Zhihao Xia
paper_title: Restoration by Generation with Constrained Priors
paper_summary: The inherent generative power of denoising diffusion models makes them
  well-suited for image restoration tasks where the objective is to find the
  optimal high-quality image within the generative space that closely resembles
  the input image. We propose a method to adapt a pretrained diffusion model for
  image restoration by simply adding noise to the input image to be restored and
  then denoise. Our method is based on the observation that the space of a
  generative model needs to be constrained. We impose this constraint by
  finetuning the generative model with a set of anchor images that capture the
  characteristics of the input image. With the constrained space, we can then
  leverage the sampling strategy used for generation to do image restoration. We
  evaluate against previous methods and show superior performances on multiple
  real-world restoration datasets in preserving identity and image quality. We
  also demonstrate an important and practical application on personalized
  restoration, where we use a personal album as the anchor images to constrain
  the generative space. This approach allows us to produce results that
  accurately preserve high-frequency details, which previous works are unable to
  do. Project webpage: https://gen2res.github.io.
paper_ai_summary: A method for image restoration using denoising diffusion models with constraints from anchor images to preserve identity and high-frequency details is proposed, showing superior performance compared to existing methods.
paper_ai_keywords: denoising diffusion models generative space finetuning anchor images image restoration high-frequency details