Real-time, deep-learning aided lensless microscope

Traditional miniaturized fluorescence microscopes are critical tools for modern biology. Invariably, they struggle to simultaneously image with a high spatial resolution and a large field of view (FOV). Lensless microscopes offer a solution to this limitation. However, real-time visualization of samples is not possible with lensless imaging, as image reconstruction can take minutes to complete. This poses a challenge for usability, as real-time visualization is a crucial feature that assists users in identifying and locating the imaging target. The issue is particularly pronounced in lensless microscopes that operate at close imaging distances. Imaging at close distances requires shift-varying deconvolution to account for the variation of the point spread function (PSF) across the FOV. Here, we present a lensless microscope that achieves real-time image reconstruction by eliminating the use of an iterative reconstruction algorithm. The neural network-based reconstruction method we show here, achieves more than 10000 times increase in reconstruction speed compared to iterative reconstruction. The increased reconstruction speed allows us to visualize the results of our lensless microscope at more than 25 frames per second (fps), while achieving better than 7 & mu;m resolution over a FOV of 10 mm2. This ability to reconstruct and visualize samples in real-time empowers a more user-friendly interaction with lensless microscopes. The users are able to use these microscopes much like they currently do with conventional microscopes. & COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Traditional miniaturized fluorescence microscopes struggle to simultaneously achieve high spatial resolution and large field of view. Lensless microscopes offer a solution but lack real-time visualization due to slow image reconstruction. This paper presents a lensless microscope that achieves real-time image reconstruction using a neural network-based method. The reconstruction speed is increased by over 10000 times, enabling visualization at more than 25 frames per second with a resolution of better than 7 μm over a field of view of 10 mm². This real-time capability enhances user-friendly interaction with lensless microscopes.