Information-guided optimization of image-based sensorless adaptive optics methods

TL;DR

This paper introduces a Fisher information-based framework to optimize sensorless adaptive optics methods, enhancing their accuracy and efficiency across various optical systems without relying on empirical tuning.

Contribution

The authors develop a general information-guided optimization framework for sensorless AO, improving performance beyond traditional empirical approaches.

Findings

Framework improves AO correction accuracy

Enhances efficiency of sensorless AO methods

Applicable across multiple imaging modalities

Abstract

Adaptive optics (AO) are reconfigurable devices that compensate for wavefront distortions or aberrations in optical systems such as microscopes, telescopes and ophthalmoscopes. Aberrations have detrimental effects that can reduce imaging quality and compromise scientific information. Sensorless AO methods were introduced to correct aberrations without a separate wavefront sensor, inferring wavefront-related information directly from phase-diverse sample images. Most sensorless AO control systems, although effective and flexible to use, were operated based on empirical experience with suboptimal performance. In this paper, we introduced a Fisher information-based analysis framework to provide information-guided method optimization. Results suggested that our framework can effectively improve the accuracy and efficiency of different sensorless AO methods. The framework is not specific to any AO method or imaging modality and has the potential to benefit a wide range of applications.