| title | SP^3: Spherical Priors for Plug-and-Play Restoration |
|---|---|
| authors | Sean Man, Ron Raphaeli, Matan Kleiner, Or Ronai |
| date | 2026-06-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.16396 |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 3-630times faster. |
cite
@misc{man2026sp,
title={SP^3: Spherical Priors for Plug-and-Play Restoration},
author={Sean Man and Ron Raphaeli and Matan Kleiner and Or Ronai},
year={2026},
eprint={2606.16396},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.16396}
}