In:
Optics Continuum, Optica Publishing Group, Vol. 2, No. 4 ( 2023-04-15), p. 727-
Kurzfassung:
Light-field three-dimensional (3D) fluorescence microscopes can acquire 3D fluorescence images in a single shot, and followed numerical reconstruction can realize cross-sectional imaging at an arbitrary depth. The typical configuration that uses a lens array and a single image sensor has the trade-off between depth information acquisition and spatial resolution of each cross-sectional image. The spatial resolution of the reconstructed image degrades when depth information increases. In this paper, we use U-net as a deep learning model to improve the quality of reconstructed images. We constructed an optical system that integrates a light-field microscope and an epifluorescence microscope, which acquire the light-field data and high-resolution two-dimensional images, respectively. The high-resolution images from the epifluorescence microscope are used as ground-truth images for the training dataset for deep learning. The experimental results using fluorescent beads with a size of 10 µm and cultured tobacco cells showed significant improvement in the reconstructed images. Furthermore, time-lapse measurements were demonstrated in tobacco cells to observe the cell division process.
Materialart:
Online-Ressource
ISSN:
2770-0208
DOI:
10.1364/OPTCON.481845
Sprache:
Englisch
Verlag:
Optica Publishing Group
Publikationsdatum:
2023
ZDB Id:
3143831-3
