Learned Regularizers for Optimization-Based Image Reconstruction

Abstract

One approach to image reconstruction from observed data is to minimize an objective function composed of a data fidelity term based on the observation model and a regularization term reflecting the properties of the original images. While various regularization functions for image restoration have been manually designed, recent studies have proposed data-driven methods that employ regularizers learned from data. This article provides an overview of such approaches based on learned regularization functions and their characteristics. Furthermore, we present simulation results that demonstrate the application of learned regularizers to computed tomography (CT) image reconstruction.