Image Reconstruction from Astronomical Interferometers using Implicit Neural Representations
Channel: COSPAR2021 Workshops
Date: 2021-02-02

Authors: Benjamin Eckart, Chao Liu, Benjamin Wu

Abstract: Astronomical interferometry enables a collection of telescopes to achieve angular resolutions comparable to that of a single, much larger telescope. This is achieved by combining simultaneous observations from pairs of telescopes such that the signal is mathematically equivalent to sampling the Fourier domain of the object. However, reconstructing images from such sparse sampling is a challenging and ill-posed problem, with current methods requiring precise tuning of parameters and manual, iterative cleaning by experts. We present a novel deep learning approach in which the representation in Fourier domain of the astronomical source is learned implicitly. Initial tests on synthetically observed MNIST digits show promising results in which the neural implicit function reconstructs images in the spatial and frequency domain approaching that of the ground truth.