# Differentiable model-based adaptive optics with transmitted and reflected light

arXiv: https://arxiv.org/abs/2007.13400 · HTML: https://mlsanity.com/paper/2007.13400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Differentiable model-based adaptive optics for two-photon microscopy](https://mlsanity.com/paper/2104.14308/) (2021)
- [Differentiable optimization of the Debye-Wolf integral for light shaping and adaptive optics in two-photon microscopy](https://mlsanity.com/paper/2211.16930/) (2022)

## Abstract (arXiv)

Aberrations limit optical systems in many situations, for example when imaging in biological tissue. Machine learning offers novel ways to improve imaging under such conditions by learning inverse models of aberrations. Learning requires datasets that cover a wide range of possible aberrations, which however becomes limiting for more strongly scattering samples, and does not take advantage of prior information about the imaging process. Here, we show that combining model-based adaptive optics with the optimization techniques of machine learning frameworks can find aberration corrections with a small number of measurements. Corrections are determined in a transmission configuration through a single aberrating layer and in a reflection configuration through two different layers at the same time. Additionally, corrections are not limited by a predetermined model of aberrations (such as combinations of Zernike modes). Focusing in transmission can be achieved based only on reflected light, compatible with an epidetection imaging configuration.