Principles of image reconstruction in optical interferometry: tutorial

TL;DR

This tutorial introduces the fundamental principles of image reconstruction in optical interferometry, explaining models, challenges with sparse data, and regularization techniques to aid astronomers in algorithm selection and image interpretation.

Contribution

It provides a comprehensive framework that unifies existing algorithms and clarifies their application in optical interferometry image reconstruction.

Findings

Most algorithms can be understood within a common framework

Regularization effects are crucial for image quality

Understanding the model aids in correct image interpretation

Abstract

This paper provides a general introduction to the problem of image reconstruction from interferometric data. A simple model of the interferometric observables is given and the issues arising from sparse Fourier data are discussed. The effects of various regularizations are described. In the proposed general framework, most existing algorithms can be understood. For an astronomer, such an understanding is crucial not only for selecting and using an algorithm but also to ensure correct interpretation of the resulting image.