Real System Dataset for "Snapshot ptychography on array cameras"

We use convolutional neural networks to recover images optically down-sampled by 6.7 × 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 signal-to-noise ratio (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.

Link to paper: Chengyu Wang, Minghao Hu, Yuzuru Takashima, Timothy J. Schulz, and David J. Brady, "Snapshot ptychography on array cameras," Opt. Express 30, 2585-2598 (2022). https://doi.org/10.1364/OE.447499.

Files:

• real_system_dataset_ground_truth: 27,000 binary ground truth images
• real_system_dataset_measurements: 27,000 measurement images captured by array cameras

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu