Learned Interferometric Imaging for the SPIDER Instrument

TL;DR

This paper introduces two deep learning-based methods for rapid, high-quality image reconstruction in the SPIDER interferometric imaging device, enabling real-time imaging and effective transfer learning in data-scarce domains.

Contribution

The work presents novel data-driven approaches that significantly improve reconstruction speed and quality for the SPIDER instrument, surpassing traditional optimization methods.

Findings

Reconstruction time reduced to ~10 milliseconds.

Deep learning methods outperform traditional optimization in quality.

Transfer learning enables application in data-scarce domains.

Abstract

The Segmented Planar Imaging Detector for Electro-Optical Reconnaissance (SPIDER) is an optical interferometric imaging device that aims to offer an alternative to the large space telescope designs of today with reduced size, weight and power consumption. This is achieved through interferometric imaging. State-of-the-art methods for reconstructing images from interferometric measurements adopt proximal optimization techniques, which are computationally expensive and require handcrafted priors. In this work we present two data-driven approaches for reconstructing images from measurements made by the SPIDER instrument. These approaches use deep learning to learn prior information from training data, increasing the reconstruction quality, and significantly reducing the computation time required to recover images by orders of magnitude. Reconstruction time is reduced to ${\sim} 10$ milliseconds, opening up the possibility of real-time imaging with SPIDER for the first time. Furthermore, we show that these methods can also be applied in domains where training data is scarce, such as astronomical imaging, by leveraging transfer learning from domains where plenty of training data are available.