New Plug-and-Play Framework Improves Image Restoration Using Stochastic Denoising
Researchers have developed SNORE (Stochastic deNOising REgularization), a new framework for image restoration that applies denoising at appropriate noise levels rather than on progressively cleaner images. The method combines physical models with deep neural networks and addresses limitations in existing Plug-and-Play algorithms that use denoisers in non-standard ways. The approach shows competitive performance on image deblurring and inpainting tasks while providing theoretical convergence guarantees.
SNORE is a novel Plug-and-Play algorithm framework designed to solve ill-posed inverse problems in image restoration, such as deblurring and inpainting. Unlike traditional PnP methods that apply denoisers to images with decreasing noise levels throughout iterations, SNORE applies the denoiser only when images contain noise at appropriate levels, aligning more closely with recent diffusion model approaches. The framework is grounded in explicit stochastic regularization and employs a stochastic gradient descent algorithm, with accompanying convergence analysis for both the base algorithm and its annealing extension. Experimental validation demonstrates that SNORE achieves competitive results compared to state-of-the-art methods on standard benchmarks, with improvements shown both in quantitative metrics and visual quality.
What's missing
The paper does not discuss computational complexity or runtime comparisons with competing methods, nor does it address practical implementation considerations such as hyperparameter sensitivity or applicability to real-world image restoration scenarios beyond the tested deblurring and inpainting tasks.
What different sources said
- arXiv stat.MLCenter
Plug-and-Play image restoration with Stochastic deNOising REgularization
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