Learned Interferometric Imaging for the SPIDER Instrument
This work addresses the bottleneck of slow image reconstruction for optical interferometry in space-based reconnaissance, offering a practical improvement for real-time applications.
The paper tackled the problem of computationally expensive image reconstruction for the SPIDER interferometric imaging instrument by introducing two data-driven deep learning approaches, which increased reconstruction quality and reduced computation time to ~10 milliseconds, enabling real-time imaging.
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.