Blind Image Restoration via Fast Diffusion Inversion

• URL: http://arxiv.org/abs/2405.19572v2
• Date: Tue, 05 Nov 2024 12:21:24 GMT
• Title: Blind Image Restoration via Fast Diffusion Inversion
• Authors: Hamadi Chihaoui, Abdelhak Lemkhenter, Paolo Favaro,
• Abstract summary: Blind Image Restoration via fast Diffusion (BIRD) is a blind IR method that jointly optimize for the degradation model parameters and the restored image. A key idea in our method is not to modify the reverse sampling, i.e., not to alter all the intermediate latents, once an initial noise is sampled. We experimentally validate BIRD on several image restoration tasks and show that it achieves state of the art performance on all of them.
• Score: 17.139433082780037
• License:
• Abstract: Image Restoration (IR) methods based on a pre-trained diffusion model have demonstrated state-of-the-art performance. However, they have two fundamental limitations: 1) they often assume that the degradation operator is completely known and 2) they alter the diffusion sampling process, which may result in restored images that do not lie onto the data manifold. To address these issues, we propose Blind Image Restoration via fast Diffusion inversion (BIRD) a blind IR method that jointly optimizes for the degradation model parameters and the restored image. To ensure that the restored images lie onto the data manifold, we propose a novel sampling technique on a pre-trained diffusion model. A key idea in our method is not to modify the reverse sampling, i.e, not to alter all the intermediate latents, once an initial noise is sampled. This is ultimately equivalent to casting the IR task as an optimization problem in the space of the input noise. Moreover, to mitigate the computational cost associated with inverting a fully unrolled diffusion model, we leverage the inherent capability of these models to skip ahead in the forward diffusion process using large time steps. We experimentally validate BIRD on several image restoration tasks and show that it achieves state of the art performance on all of them. Our code is available at https://github.com/hamadichihaoui/BIRD.

Related papers

• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution [52.47005445345593]
  BlindDiff is a DM-based blind SR method to tackle the blind degradation settings in SISR. BlindDiff seamlessly integrates the MAP-based optimization into DMs. Experiments on both synthetic and real-world datasets show that BlindDiff achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-15T11:21:34Z)
• Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data [56.81246107125692]
  Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption. We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance. We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
  arXiv  Detail & Related papers  (2024-03-13T17:28:20Z)
• Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise [34.65659277870287]
  Research on denoising diffusion models has expanded its application to the field of image restoration. We propose Resfusion, a framework that incorporates the residual term into the diffusion forward process. We show that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps.
  arXiv  Detail & Related papers  (2023-11-25T02:09:38Z)
• Prompt-tuning latent diffusion models for inverse problems [72.13952857287794]
  We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Our method, called P2L, outperforms both image- and latent-diffusion model-based inverse problem solvers on a variety of tasks, such as super-resolution, deblurring, and inpainting.
  arXiv  Detail & Related papers  (2023-10-02T11:31:48Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• ADIR: Adaptive Diffusion for Image Reconstruction [46.838084286784195]
  We propose a conditional sampling scheme that exploits the prior learned by diffusion models. We then combine it with a novel approach for adapting pretrained diffusion denoising networks to their input. We show that our proposed adaptive diffusion for image reconstruction' approach achieves a significant improvement in the super-resolution, deblurring, and text-based editing tasks.
  arXiv  Detail & Related papers  (2022-12-06T18:39:58Z)
• Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis [39.671396431940224]
  diffusion models have shown remarkable results in image synthesis by gradually removing noise and amplifying signals. We propose a novel generative process that synthesizes images in a coarse-to-fine manner. Experiments show that the proposed model outperforms the previous method in FID on LSUN bedroom and church datasets.
  arXiv  Detail & Related papers  (2022-07-16T15:00:21Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.