Super-resolution wavefront reconstruction
Sylvain Oberti, Carlos Correia, Thierry Fusco, Benoit Neichel and, Pierre Guiraud

TL;DR
This paper introduces a super-resolution approach to bi-dimensional wavefront reconstruction, enhancing adaptive optics systems by combining multiple wavefront sensor samples to improve resolution without increasing noise.
Contribution
It demonstrates how super-resolution techniques can be applied to wavefront sensors, enabling higher resolution reconstruction and more flexible adaptive optics system designs.
Findings
Super-resolution improves the number of accessible wavefront modes.
Noise propagation remains unaffected by super-resolution.
The approach extends to Pyramid Wavefront Sensors, broadening its applicability.
Abstract
Super-Resolution (SR) is a technique that seeks to upscale the resolution of a set of measured signals. SR retrieves higher-frequency signal content by combining multiple lower resolution sampled data sets. SR is well known both in the temporal and spatial domains. It is widely used in imaging to reduce aliasing and enhance the resolution of coarsely sampled images.This paper applies the SR technique to the bi-dimensional wavefront reconstruction. In particular, we show how SR is intrinsically suited for tomographic multi WaveFront Sensor (WFS) AO systems revealing many of its advantages with minimal design effort. This paper provides a direct space and Fourier-optics description of the wavefront sensing operation and demonstrate how SR can be exploited through signal reconstruction, especially in the framework of Periodic Nonuniform Sampling. Both meta uniform and nonuniform sampling…
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Taxonomy
TopicsAdaptive optics and wavefront sensing · Optical measurement and interference techniques · Advanced Optical Sensing Technologies
