Document Type
Article
Publication Date
1-21-2019
Published In
Optics Express
Abstract
Fourier ptychographic microscopy allows for the collection of images with a high space-bandwidth product at the cost of temporal resolution. In Fourier ptychographic microscopy, the light source of a conventional widefield microscope is replaced with a light-emitting diode (LED) matrix, and multiple images are collected with different LED illumination patterns. From these images, a higher-resolution image can be computationally reconstructed without sacrificing field-of-view. We use deep learning to achieve single-shot imaging without sacrificing the space-bandwidth product, reducing the acquisition time in Fourier ptychographic microscopy by a factor of 69. In our deep learning approach, a training dataset of high-resolution images is used to jointly optimize a single LED illumination pattern with the parameters of a reconstruction algorithm. Our work paves the way for high-throughput imaging in biological studies.
Recommended Citation
Yi Fei Cheng , '21; Megan Strachan , '21; Zachary Weiss , '20; Moniher Deb , '19; Dawn M. Carone; and Vidya Ganapati.
(2019).
"Illumination Pattern Design With Deep Learning For Single-Shot Fourier Ptychographic Microscopy".
Optics Express.
Volume 27,
Issue 2.
644-65.
DOI: 10.1364/OE.27.000644
https://works.swarthmore.edu/fac-biology/580