Second Harmonic Imaging Enhanced by Deep Learning Decipher
Abstract
Wavefront sensing and reconstruction are widely used for adaptive optics, aberration correction, and high-resolution optical phase imaging. Traditionally, interference and/or microlens arrays are used to convert the optical phase into intensity variation. Direct imaging of distorted wavefront usually results in complicated phase retrieval with low contrast and low sensitivity. Here, a novel approach has been developed and experimentally demonstrated based on the phase-sensitive information encoded into second harmonic signals, which are intrinsically sensitive to wavefront modulations. By designing and implementing a deep neural network, we demonstrate the second harmonic imaging enhanced by deep learning decipher (SHIELD) for efficient and resilient phase retrieval. Inheriting the advantages of two-photon microscopy, SHIELD demonstrates single-shot, reference-free, and video-rate phase imaging with sensitivity better than {\lambda}/100 and high robustness against noises, facilitating numerous applications from biological imaging to wavefront sensing.
Cite
@article{arxiv.2010.08211,
title = {Second Harmonic Imaging Enhanced by Deep Learning Decipher},
author = {Weiru Fan and Tianrun Chen and Eddie Gil and Shiyao Zhu and Vladislav Yakovlev and Da-Wei Wang and Delong Zhang},
journal= {arXiv preprint arXiv:2010.08211},
year = {2021}
}