Joint optical-digital design strategy for adaptive optics systems: application to wavelength selection for satellite imaging

TL;DR

This paper addresses end-to-end optimization of AO-assisted satellite imaging by introducing a joint optical/digital co-design framework that accounts for the entire acquisition and restoration chain. It defines an RNMSE-based objective, enabling fast, analytical evaluation via a Wiener MMSE restoration and a Gaussian object prior, to optimize wavelength distribution between the WFS and the imaging channel. The authors show that the imaging wavelength optimum shifts with seeing and object brightness and demonstrate improvements from jointly optimizing wavelength and bandwidth, including a robust, multi-channel (two-channel) design that performs nearly as well as a dynamic, real-time solution across varied conditions. The work provides a practical, scalable approach to robust AO system design and highlights avenues for extending co-design to additional AO parameters and experimental validation.

Abstract

Adaptive optics can be used to mitigate the effects of atmospheric turbulence on imaging systems, but the correction is only partial, and deconvolution is often required to improve the resolution. This results in entire optical/digital systems, which are traditionally designed sequentially, i.e. , the adaptive optics system is optimised first, and the restoration algorithms are designed a second time. Studies on optical/digital systems have shown that jointly optimizing the whole system is a better alternative. We propose to extend these co-design strategies to the design of an adaptive optics-assisted imaging system. We derive a simple criterion that takes into account the source properties and the entire optical/ digital system performance. To illustrate its interest, we use it to optimize the wavelength distribution between the wavefront sensor and the imaging camera. In addition, we explore the potential of using multiple imaging channels operating at different wavelengths as a means of making an imaging system robust to turbulence strength and source magnitude variations. Later, any parameter of the optical/digital system, if not the entire system itself, could be optimized this way.

Figures (12)

• Figure 1: Simulation of the AO-corrected (filled lines) & diffraction limited (dashed lines) OTFs modulus of the PROVIDENCE-like system described in Sect. \ref{['sec:setup']}, for different imaging wavelengths, assuming a seeing of 2". The simulation details are described in Sect. \ref{['sec:setup']}.
• Figure 2: Root normalised MSE error depending on $\lambda_{ima}$ for different seeings and object magnitude ($\Delta\lambda=50$ nm). The black dots indicate the minimum of each curve.
• Figure 3: NMSE spectrum for different imaging wavelengths $\lambda_{ima}$ (seeing=2"; $M^v$=4; $\Delta\lambda=50$ nm).
• Figure 4: Simulation of an observation of the satellite Envisat at $\lambda_{ima}$=616 nm (seeing=2"; $M^v$=4). Left: True object; middle: AO-corrected focal plane image; right: Wiener-restored image.
• Figure 5: Simulated images, after deconvolution, of observations of the satellite Envisat at different wavelengths (seeing=2"; $M^v$=4). The images are zoomed in.