Data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms, require large datasets. In this work, we introduce a data acquisition pipeline that can capture from multiple lensless imaging systems in parallel, under the same imaging conditions, and paired with computational ground truth registration. We provide an open-access 25,000 image dataset with two lensless imagers, a reproducible hardware setup, and open-source camera synchronization code. Experimental datasets from our system can enable data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms and end-to-end system design.
@article{arxiv.2501.13334,
title = {Scalable dataset acquisition for data-driven lensless imaging},
author = {Clara S. Hung and Leyla A. Kabuli and Vasilisa Ponomarenko and Laura Waller},
journal= {arXiv preprint arXiv:2501.13334},
year = {2026}
}
Comments
5 pages, 3 figures, to be published in SPIE Photonics West 2025 Proceedings