SP$^3$: Spherical Priors for Plug-and-Play Restoration
In this paper, we introduce SP$^3$, a novel Plug-and-Play algorithm that accelerates maximum a posteriori image restoration by replacing denoisers with Spherical Encoders (SE) as generative priors. SP$^3$ approximates the intractable proximal prior step by utilizing the SE tightly structured latent space as a robust projection onto the natural image manifold. Alternating this projection with a closed-form data-consistency step, via Half-Quadratic Splitting, achieves stable convergence without requiring gradient computation during inference. This unique formulation unlocks "anytime" restoration capabilities, producing sharp, plausible images from the first iteration. Evaluations across a variety of image restoration tasks demonstrate that SP$^3$ achieves perceptual quality comparable to state-of-the-art zero-shot diffusion and flow methods while being $3$-$630\times$ faster.
Code (0)
등록된 구현이 없습니다.
Tasks
Image RestorationSimilar Papers 제목 키워드 기반
Integrating Reweighted Least Squares with Plug-and-Play Diffusion Priors for Noisy Image Restoration
Existing plug-and-play image restoration methods typically employ off-the-shelf Gaussian denoisers as proximal operators within classical optimization frameworks based on variable splitting. Recently, denoisers induced b…
Image RestorationProvably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug…
DeblurringSuper-ResolutionInvestigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images
Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generative priors for natural as well as medical images. However, solutions often…
Super-ResolutionFiRe: Fixed-points of Restoration Priors for Solving Inverse Problems
Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on denoising neural networks have become cen…
DenoisingPnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent
The Plug-and-Play (PnP) framework makes it possible to integrate advanced image denoising priors into optimization algorithms, to efficiently solve a variety of image restoration tasks generally formulated as Maximum A P…
DenoisingImage DenoisingImage Restoration