SP$^3$: Spherical Priors for Plug-and-Play Restoration
For image restoration tasks, SP$^3$ offers a faster alternative to zero-shot diffusion and flow methods without sacrificing perceptual quality.
SP$^3$ introduces a Plug-and-Play algorithm using Spherical Encoders as generative priors for image restoration, achieving perceptual quality comparable to state-of-the-art diffusion and flow methods while being 3–630× faster.
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.