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arXiv · 2010.08211

Second Harmonic Imaging Enhanced by Deep Learning Decipher

Weiru Fan·Tianrun Chen·Eddie Gil·Shiyao Zhu·Vladislav Yakovlev·Da-Wei Wang·Delong Zhang
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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.

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Weiru Fan, Tianrun Chen, Eddie Gil, Shiyao Zhu, Vladislav Yakovlev, Da-Wei Wang, Delong Zhang. 2020-10-16. Second Harmonic Imaging Enhanced by Deep Learning Decipher. https://doi.org/10.1021/acsphotonics.1c00395

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