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arXiv · 2606.16396

SP$^3$: Spherical Priors for Plug-and-Play Restoration

Sean Man·Ron Raphaeli·Matan Kleiner·Or Ronai
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Abstract

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.

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Sean Man, Ron Raphaeli, Matan Kleiner, Or Ronai. 2026-06-15. SP$^3$: Spherical Priors for Plug-and-Play Restoration. https://arxiv.org/abs/2606.16396

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