Plug-and-Play priors for model based reconstruction

TLDR

Model‑based reconstruction solves inverse imaging problems, and recent advances in denoising and complex forward models have improved performance, yet integrating state‑of‑the‑art priors with forward models remains challenging. This paper proposes a flexible framework to match advanced imaging forward models with advanced priors or denoising models. The framework, called Plug‑and‑Play priors, enables any denoising method—even those lacking an explicit optimization formulation—to be incorporated into existing inversion algorithms. Experiments show that Plug‑and‑Play priors allow diverse denoisers to be combined with tomographic forward models, greatly expanding solvable problem classes.

Abstract

Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the forward model corresponds to complex physical measurements in applications such as X-ray CT, electron-microscopy, MRI, and ultrasound, to name just a few. However, combining state-of-the-art denoising algorithms (i.e., prior models) with state-of-the-art inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows state-of-the-art forward models of imaging systems to be matched with state-of-the-art priors or denoising models. This framework, which we term as Plug-and-Play priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows state-of-the-art denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions.