Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy

TL;DR

This paper introduces a deep learning method to optimize illumination patterns for Fourier ptychographic microscopy, enabling single-shot high-resolution imaging and significantly reducing acquisition time for biological applications.

Contribution

It presents a novel deep learning approach that jointly optimizes illumination patterns and reconstruction parameters for single-shot Fourier ptychography.

Findings

Achieved 69-fold reduction in acquisition time.

Enabled high-resolution imaging with a single shot.

Demonstrated potential for high-throughput biological imaging.

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.