  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.
