Second harmonic phase imaging enabled by deep learning

Abstract

Optical phase microscopy is widely adopted for quantitative imaging of optical density in transparent cells and tissues that lack absorption contrast. Fundamentally, the phase information of the sample is contained in the wavefront of the probe beam, often detected by interferometry-based techniques. Here, a novel approach has been developed based on the phase-sensitive second harmonic signals that are generated after the sample. A deep learning algorithm is developed for efficient recovery of the original phase images. Inheriting the advantages of the second harmonic imaging, our second harmonic phase imaging is a label-free technique with a demonstrated phase sensitivity of 1/100 wavelength and high robustness against noises, facilitating applications in biological imaging and remote sensing.