Super-resolution wavefront reconstruction in adaptive-optics with pyramid sensors

TL;DR

This paper extends super-resolution techniques to pyramid wavefront sensors in adaptive optics, enabling higher spatial frequency reconstruction and improved system robustness with minimal additional computational cost.

Contribution

It introduces a super-resolution approach for pyramid sensors, allowing reconstruction beyond traditional limits and measuring amplitude aberrations, enhancing adaptive optics performance.

Findings

Reconstruction of spatial frequencies beyond Shannon-Nyquist limit.

Super-resolved PyWFS improves resilience to mis-registration.

Performance gains with up to 2x increased computational load.

Abstract

Super-resolution (SR) refers to a combination of optical design and signal processing techniques jointly employed to obtain reconstructed wave-fronts at a higher-resolution from multiple low-resolution samples, overcoming the intrinsic limitations of the latter. After compelling examples have been provided on multi-Shack-Hartman (SH) wave-front sensor (WFS) adaptive optics systems performing atmospheric tomography with laser guide star probes, we broaden the SR concept to pyramid sensors (PyWFS) with a single sensor and a natural guide star. We revisit the analytic PyWFS diffraction model to claim two aspects: i) that we can reconstruct spatial frequencies beyond the natural Shannon-Nyquist frequency imposed by the detector pixel size and/or ii) that the PyWFS can be used to measure amplitude aberrations (at the origin of scintillation). SR offers the possibility to control a higher actuator density deformable mirror from seemingly fewer samples, the quantification of which is one of the goals of this paper. A super-resolved PyWFS is more resilient to mis-registration, lifts alignment requirements and improves performance (against aliasing and other spurious modes AO systems are poorly sensitive to) with only a factor up to 2 increased real-time computational burden.