Second Harmonic Imaging Enhanced by Deep Learning Decipher

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 nonlinear optical encoding approach has been developed and experimentally demonstrated using optical second harmonic generation to sharpen the phase information carried by the probe beam. By designing and implementing a deep neural network, we demonstrate the second harmonic imaging enhanced by a 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 noise, facilitating numerous applications from biological imaging to wavefront sensing.