Robust interferometric imaging via prior-less phase recovery: redundant spacing calibration with generalized-closure phases

Atmospheric turbulence presents a fundamental challenge to Fourier phase recovery in optical interferometry. Typical reconstruction algorithms employ Bayesian inference techniques which rely on prior knowledge of the scene under observation. In contrast, redundant spacing calibration (RSC) algorithms employ redundancy in the baselines of the interferometric array to directly expose the contribution of turbulence, thereby enabling phase recovery for targets of arbitrary and unknown complexity. Traditionally RSC algorithms have been applied directly to single-exposure measurements, which are reliable only at high photon flux in general. In scenarios of low photon flux, such as those arising in the observation of dim objects in space, one must instead rely on time-averaged, atmosphere-invariant quantities such as the bispectrum. In this paper, we develop a novel RSC-based algorithm for prior-less phase recovery in which we generalize the bispectrum to higher order atmosphere-invariants (n-spectra) for improved sensitivity. We provide a strategy for selection of a high-signal-to-noise ratio set of n-spectra using the graph-theoretic notion of the minimum cycle basis. We also discuss a key property of this set (wrap-invariance), which then enables reliable application of standard linear estimation techniques to recover the Fourier phases from the 2 pi-wrapped n-spectra phases. For validation, we analyse the expected shot-noise-limited performance of our algorithm for both pairwise and Fizeau interferometric architectures, and corroborate this analysis with simulation results showing performance near an atmosphere-oracle Cramer-Rao bound. Lastly, we apply techniques from the field of compressed sensing to perform image reconstruction from the estimated complex visibilities.