Deep learning-based virtual refocusing of images using an engineered point-spread function

TL;DR

This paper introduces a deep learning method using cascaded neural networks and engineered point-spread functions to significantly extend the depth of field in fluorescence microscopy, enabling high-resolution, volumetric imaging.

Contribution

The paper presents W-Net, a novel cascaded neural network architecture that refocuses images and enhances resolution using engineered PSFs, extending microscope DOF by ~20-fold.

Findings

Extended depth of field in fluorescence microscopy by ~20 times

Improved lateral resolution through cross-modality transformation

Applicable to localization microscopy and volumetric imaging

Abstract

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.