An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

TL;DR

This paper introduces an online plug-and-play algorithm for image reconstruction that efficiently handles large datasets using a subset of measurements per iteration, with proven convergence and practical validation.

Contribution

It presents a novel online PnP-ISTA algorithm with theoretical convergence analysis applicable to non-proximal denoisers, enabling scalable imaging reconstruction.

Findings

Algorithm achieves convergence for non-proximal denoisers.

Scalable to large datasets with subset measurements.

Effective in diffraction tomography image reconstruction.

Abstract

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the iterative shrinkage/thresholding algorithm (ISTA). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-ISTA, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.