Plug-and-Play image restoration with Stochastic deNOising REgularization
Plug-and-Play (PnP) algorithms apply denoisers to progressively noise-decaying iterates, conflicting with diffusion models (DMs), which deploy denoisers exclusively on controllably noisy data. This inconsistency undermines theoretical alignment and practical performance. Method: We propose SNORE—a stochastic noise-level-adaptive regularization framework for image inverse problems (e.g., deblurring, inpainting). SNORE constructs a noise-level-matched stochastic gradient descent optimizer via explicit noise-aware regularization. Contribution/Results: This is the first PnP method to incorporate noise-perceptive stochastic regularization, unifying PnP and DM denoising logic while providing rigorous convergence and annealing-theoretic analysis. Experiments demonstrate that SNORE, when integrated with deep denoisers (e.g., DnCNN), achieves state-of-the-art performance on deblurring and inpainting—outperforming prior methods in PSNR, SSIM, and perceptual quality.