We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF). This network model, referred to as W-Net, is composed of two cascaded generator and discriminator network pairs. The first generator network learns to virtually refocus an input image onto a user-defined plane, while the second generator learns to perform a cross-modality image transformation, improving the lateral resolution of the output image. Using this W-Net model with DH-PSF engineering, we extend the DOF of a fluorescence microscope by ~20-fold. This approach can be applied to develop deep learning-enabled image reconstruction methods for localization microscopy techniques that utilize engineered PSFs to improve their imaging performance, including spatial resolution and volumetric imaging throughput.
@article{arxiv.2012.11892,
title = {Deep learning-based virtual refocusing of images using an engineered point-spread function},
author = {Xilin Yang and Luzhe Huang and Yilin Luo and Yichen Wu and Hongda Wang and Yair Rivenson and Aydogan Ozcan},
journal= {arXiv preprint arXiv:2012.11892},
year = {2021}
}