Snapshot Ptychography on Array cameras

TL;DR

This paper demonstrates a neural network-based approach for high-resolution image recovery from a single snapshot in array camera systems, reducing the need for scanning and calibration, and exploring optimization strategies.

Contribution

It introduces a decompressive neural estimation method for snapshot ptychography that eliminates calibration, enabling rapid high-resolution imaging with array cameras.

Findings

Achieved 6.7× optical down-sampling recovery

Single snapshot can be used for high-quality image reconstruction

Simulations suggest strategies for optimizing array camera sampling

Abstract

We use convolutional neural networks to recover images optically down-sampled by $6.7\times$ using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, here we apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve SNR. In place training on experimental measurements eliminates the need to directly calibrate the measurement system. We also present simulations of diverse array camera sampling strategies to explore how snapshot compressive systems might be optimized.