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 [e-mail]

本研究采用卷积神经网络，通过在由16个相机阵列构成的系统中实施相干孔径合成，恢复由6.7倍光学下采样得到的图像。与传统基于扫描和过采样的相干衍射成像技术不同，本研究中我们应用了解压缩神经网络估计方法，从单一快照中恢复全分辨率图像，尽管如仿真所示，使用多个快照可以提高信噪比（SNR）。在实验测量数据上的就地训练消除了对直接校准测量系统的需求。此外，我们还展示了不同阵列相机采样策略的仿真，以探讨快照压缩系统可能的最优化路径。