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.

Automated summary

Wavefront sensing and reconstruction are widely used in various applications including adaptive optics and high-resolution imaging. A novel nonlinear optical encoding method using optical second harmonic generation has been developed to enhance phase imaging sensitivity and efficiency. By utilizing deep neural networks, the method demonstrates efficient phase retrieval with high robustness.