Convergent Plug-and-Play Image Restoration with Annealed Noise Levels

arXiv:2609.32393 · math.OC, cs.LG · Submitted 2026-09-26 · Read on arXiv

math.OC, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/samuro95/annealed-pnp

Terminology

• Regularization — 26× in this paper · explained in 3 episodes
• Inverse Problem — 12× in this paper · explained in 2 episodes
• Convergence Guarantees — 8× in this paper · explained in 3 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• International Conference on Computer Vision — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Lyapunov function — 1× in this paper · explained in 2 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications
• A Variational Perspective on Solving Inverse Problems with Diffusion Models
• Stochastic Generative Plug-and-Play Priors
• MAP Estimation with Denoisers: Convergence Rates and Guarantees
• Regularization by Denoising: Clarifications and New Interpretations
• Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
• Score-Based Generative Modeling through Stochastic Differential Equations
• DeepInverse: A Python package for solving imaging inverse problems with deep learning
• Learning Deep CNN Denoiser Prior for Image Restoration

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