Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy
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.
Cite
@article{arxiv.1810.03481,
title = {Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy},
author = {Yi Fei Cheng and Megan Strachan and Zachary Weiss and Moniher Deb and Dawn Carone and Vidya Ganapati},
journal= {arXiv preprint arXiv:1810.03481},
year = {2019}
}