New Plug-and-Play Framework Improves Image Restoration Using Stochastic Denoising
Researchers have proposed SNORE (Stochastic deNOising REgularization), a new Plug-and-Play image restoration framework that applies a denoiser only to images with appropriately matched noise levels. Unlike conventional PnP algorithms, SNORE bridges the gap with Diffusion Model-based approaches by grounding regularization in an explicit stochastic formulation, yielding a stochastic gradient descent solver for ill-posed inverse problems. The method demonstrates competitive or superior performance on standard deblurring and inpainting benchmarks, with convergence guarantees provided for both the base algorithm and an annealing extension.
Plug-and-Play (PnP) algorithms are widely used for image inverse problems, combining physical forward models with deep neural network denoisers as implicit regularizers. A known inconsistency in classical PnP methods is that the denoiser is applied to images at varying—and often inappropriately low—noise levels as iterations progress, diverging from how denoisers are trained. SNORE addresses this by ensuring the denoiser is always applied to images re-noised to the correct level, drawing inspiration from Diffusion Model pipelines while remaining within the PnP paradigm. The framework introduces an explicit stochastic regularization term, from which a stochastic gradient descent algorithm is derived to solve ill-posed problems such as deblurring and inpainting. The authors provide a formal convergence analysis covering both the standard algorithm and an annealing variant that gradually reduces noise levels. Experimental results show SNORE is competitive with state-of-the-art methods both quantitatively (e.g., PSNR/SSIM metrics) and qualitatively in visual output. The preprint has undergone multiple revisions since its initial submission in February 2024, suggesting ongoing refinement.
What's missing
The paper does not appear to report computational cost or runtime comparisons against baseline PnP and Diffusion Model methods, which would be important for assessing practical deployability. It is also unclear whether SNORE has been evaluated on tasks beyond deblurring and inpainting (e.g., super-resolution, compressed sensing) or on out-of-distribution image types. The choice of backbone denoiser architecture and its sensitivity to that choice are not discussed in the abstract.
What different sources said
- arXiv stat.MLCenter
Plug-and-Play image restoration with Stochastic deNOising REgularization
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