REVIEW 7 cited by
Decoupled Data Consistency with Diffusion Purification for Image Restoration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability to model data distributions. To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models. However, the additional gradient steps pose a challenge for real-world practical applications as they incur a large computational overhead, thereby increasing inference time. They also present additional difficulties when using accelerated diffusion model samplers, as the number of data consistency steps is limited by the number of reverse sampling steps. In this work, we propose a novel diffusion-based image restoration solver that addresses these issues by decoupling the reverse process from the data consistency steps. Our method involves alternating between a reconstruction phase to maintain data consistency and a refinement phase that enforces the prior via diffusion purification. Our approach demonstrates versatility, making it highly adaptable for efficient problem-solving in latent space. Additionally, it reduces the necessity for numerous sampling steps through the integration of consistency models. The efficacy of our approach is validated through comprehensive experiments across various image restoration tasks, including image denoising, deblurring, inpainting, and super-resolution.
Forward citations
Cited by 7 Pith papers
-
DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
DAPS++ decouples diffusion-model initialization from measurement-consistency refinement to solve inverse problems with fewer steps while preserving reconstruction quality.
-
DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Noise-perturbed condition injection plus contrastive trajectory refinement improves training-free conditional diffusion sampling across style transfer, super-resolution and deblurring.
-
Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration
LEADer adaptively modulates prior strength and sampling step size using local epistemic uncertainty, improving quality and speed of plug-and-play diffusion image restoration.
-
Enhancing Diffusion Model Stability for Image Restoration via Gradient Management
SPGD combines a progressive likelihood warm-up with adaptive directional momentum to reduce gradient conflicts and fluctuations in diffusion-based image restoration, improving metrics over existing baselines.
-
Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
A weighted-particle sampler evolves the posterior through the diffusion model's reverse dynamics, with theoretical error bounds and improved image reconstructions.
-
Local MAP Sampling for Diffusion Models
LMAPS frames reverse-diffusion inverse-problem solving as repeated local MAP estimation, unifying existing optimization-based solvers, and achieves strong PSNR gains on tasks like motion deblurring, JPEG restoration, ...
-
Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint
Guided Decoupled Posterior Sampling (GDPS) adds a gradient step on the measurement mismatch ||y - A(x_t)||^2 during the reverse process, improving reconstruction accuracy over DAPS, SITCOM, Resample, and DPS.
Discussion (0). Continue with ORCID to comment.