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Proceedings Paper

Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy (Conference Presentation)
Author(s): Vidya Ganapati

Paper 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.

Paper Details

Date Published: 13 May 2019
PDF
Proc. SPIE 10990, Computational Imaging IV, 109900G (13 May 2019); doi: 10.1117/12.2520317
Show Author Affiliations
Vidya Ganapati, Swarthmore College (United States)


Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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