Plug-and-Play image restoration with Stochastic deNOising REgularization

2024-02-01

International Conference on Machine Learning

AI Summary

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.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images. We propose a new PnP framework, called Stochastic deNOising REgularization (SNORE), which applies the denoiser only on images with noise of the adequate level. It is based on an explicit stochastic regularization, which leads to a stochastic gradient descent algorithm to solve ill-posed inverse problems. A convergence analysis of this algorithm and its annealing extension is provided. Experimentally, we prove that SNORE is competitive with respect to state-of-the-art methods on deblurring and inpainting tasks, both quantitatively and qualitatively.

Problem

• Improves Plug-and-Play image restoration with noise-level-appropriate denoising
• Addresses non-standard denoiser use in iterative image restoration algorithms
• Proposes stochastic regularization for solving ill-posed inverse problems

Innovation

• Plug-and-Play with stochastic denoising regularization
• Denoiser applied only on adequately noisy images
• Stochastic gradient descent for ill-posed problems