Plug-In Stochastic Gradient Method

2018/11/08 by Yu Sun, Brendt Wohlberg, Sun, Yu +3
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1811.03659

openalex publication_date 2018/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm. In this paper, we discuss our recent online variant of PnP that uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We additionally present novel convergence results for both batch and online PnP algorithms.

Citations

• Fast Image Recovery Using Variable Splitting and Constrained Optimization

Related

• Scalable Plug-and-Play ADMM with Convergence Guarantees
• Plug-and-Play Priors as a Score-Based Method
• Analysis Plug-and-Play Methods for Imaging Inverse Problems
• Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling
• You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors