Publications
Ruiming Cao; Nikita S. Divekar; James K. Nuñez; Srigokul Upadhyayula; Laura Waller
Neural space–time model for dynamic multi-shot imaging Journal Article
In: Nature Methods, pp. 1–6, 2024, ISSN: 1548-7105, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: Imaging, Phase-contrast microscopy, Super-resolution microscopy
@article{cao_neural_2024,
title = {Neural space–time model for dynamic multi-shot imaging},
author = {Ruiming Cao and Nikita S. Divekar and James K. Nuñez and Srigokul Upadhyayula and Laura Waller},
url = {https://www.nature.com/articles/s41592-024-02417-0},
doi = {10.1038/s41592-024-02417-0},
issn = {1548-7105},
year = {2024},
date = {2024-09-24},
urldate = {2024-09-01},
journal = {Nature Methods},
pages = {1--6},
abstract = {Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space–time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.},
note = {Publisher: Nature Publishing Group},
keywords = {Imaging, Phase-contrast microscopy, Super-resolution microscopy},
pubstate = {published},
tppubtype = {article}
}
Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space–time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.