# Second Harmonic Imaging Enhanced by Deep Learning Decipher

arXiv: https://arxiv.org/abs/2010.08211 · HTML: https://mlsanity.com/paper/2010.08211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model](https://mlsanity.com/paper/2408.13061/) (2024)

## Abstract (arXiv)

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 λ/100 and high robustness against noises, facilitating numerous applications from biological imaging to wavefront sensing.