Differentiable model-based adaptive optics with transmitted and reflected light

TL;DR

This paper introduces a novel approach combining model-based adaptive optics with machine learning optimization to efficiently correct aberrations in optical imaging, using minimal measurements and applicable in transmission and reflection modes.

Contribution

It presents a method that integrates adaptive optics with machine learning to find aberration corrections without relying on predefined models, applicable in complex scattering environments.

Findings

Effective aberration correction with few measurements

Works in both transmission and reflection configurations

Does not depend on predefined aberration models

Abstract

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.