Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy (Conference Presentation)

Vidya Ganapati

doi:10.1117/12.2520317

13 May 2019 Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy (Conference Presentation)

Vidya Ganapati

Author Affiliations +

Proceedings Volume 10990, Computational Imaging IV; 109900G (2019) https://doi.org/10.1117/12.2520317
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Vidya Ganapati "Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy (Conference Presentation)", Proc. SPIE 10990, Computational Imaging IV, 109900G (13 May 2019); https://doi.org/10.1117/12.2520317

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