Second Harmonic Imaging Enhanced by Deep Learning Decipher

TL;DR

This paper introduces SHIELD, a deep learning-based method that enhances second harmonic imaging for wavefront sensing, achieving high sensitivity, noise robustness, and real-time phase imaging without the need for reference signals.

Contribution

The paper presents a novel deep learning approach for phase retrieval in second harmonic imaging, improving sensitivity, robustness, and enabling real-time, reference-free imaging.

Findings

Achieves sensitivity better than λ/100.

Provides single-shot, reference-free phase imaging.

Demonstrates robustness against noise in biological and wavefront sensing applications.

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.