title: SVNR: Spatially-variant Noise Removal with Denoising Diffusion
summary: Denoising diffusion models have recently shown impressive results in
  generative tasks. By learning powerful priors from huge collections of training
  images, such models are able to gradually modify complete noise to a clean
  natural image via a sequence of small denoising steps, seemingly making them
  well-suited for single image denoising. However, effectively applying denoising
  diffusion models to removal of realistic noise is more challenging than it may
  seem, since their formulation is based on additive white Gaussian noise, unlike
  noise in real-world images. In this work, we present SVNR, a novel formulation
  of denoising diffusion that assumes a more realistic, spatially-variant noise
  model. SVNR enables using the noisy input image as the starting point for the
  denoising diffusion process, in addition to conditioning the process on it. To
  this end, we adapt the diffusion process to allow each pixel to have its own
  time embedding, and propose training and inference schemes that support
  spatially-varying time maps. Our formulation also accounts for the correlation
  that exists between the condition image and the samples along the modified
  diffusion process. In our experiments we demonstrate the advantages of our
  approach over a strong diffusion model baseline, as well as over a
  state-of-the-art single image denoising method.
paper_authors._id: 649cf1dde89a765015a0e68b
paper_authors.name: Naama Pearl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-12T07:30:00.872Z
paper_authors._id: 64ad7c56b7d86b40fd84f39b
paper_authors.avatarUrl: /avatars/6ddd82f63bc9c91996e9aa41561d63ba.svg
paper_authors.fullname: Naama Pearl
paper_authors.type: user
paper_authors.user: naamapearl
paper_authors._id: 649cf1dde89a765015a0e68c
paper_authors.name: Yaron Brodsky
paper_authors._id: 649cf1dde89a765015a0e68d
paper_authors.name: Dana Berman
paper_authors._id: 649cf1dde89a765015a0e68e
paper_authors.name: Assaf Zomet
paper_authors._id: 649cf1dde89a765015a0e68f
paper_authors.name: Alex Rav Acha
paper_authors._id: 649cf1dde89a765015a0e690
paper_authors.name: Daniel Cohen-Or
paper_authors._id: 649cf1dde89a765015a0e691
paper_authors.name: Dani Lischinski
paper_title: SVNR: Spatially-variant Noise Removal with Denoising Diffusion
paper_summary: Denoising diffusion models have recently shown impressive results in
  generative tasks. By learning powerful priors from huge collections of training
  images, such models are able to gradually modify complete noise to a clean
  natural image via a sequence of small denoising steps, seemingly making them
  well-suited for single image denoising. However, effectively applying denoising
  diffusion models to removal of realistic noise is more challenging than it may
  seem, since their formulation is based on additive white Gaussian noise, unlike
  noise in real-world images. In this work, we present SVNR, a novel formulation
  of denoising diffusion that assumes a more realistic, spatially-variant noise
  model. SVNR enables using the noisy input image as the starting point for the
  denoising diffusion process, in addition to conditioning the process on it. To
  this end, we adapt the diffusion process to allow each pixel to have its own
  time embedding, and propose training and inference schemes that support
  spatially-varying time maps. Our formulation also accounts for the correlation
  that exists between the condition image and the samples along the modified
  diffusion process. In our experiments we demonstrate the advantages of our
  approach over a strong diffusion model baseline, as well as over a
  state-of-the-art single image denoising method.
paper_ai_keywords: denoising diffusion models generative tasks powerful priors training images noise denoising steps white Gaussian noise SVNR spatially-variant noise model time embedding training schemes denoising diffusion models training images spatially-variant noise model time embedding training schemes