Post-AO high-resolution imaging using the Kraken multi-frame blind deconvolution algorithm

TL;DR

This paper introduces the Kraken multi-frame blind deconvolution algorithm for high-resolution imaging in extreme adaptive optics, enabling diffraction-limited source estimation from high-cadence telescope data.

Contribution

The paper presents a novel MFBD algorithm that guarantees convergence and improves high-resolution imaging in ExAO systems, demonstrated on a spectroscopic binary.

Findings

Successful high-resolution reconstruction of α Andromedae

Algorithm guarantees convergence with a robust initial guess

Applicable to high-contrast imaging with large telescopes

Abstract

In the context of extreme adaptive optics (ExAO) for large telescopes, we present the Kraken multi-frame blind deconvolution (MFBD) algorithm for processing high-cadence acquisitions, capable to provide a diffraction-limited estimation of the source brightness distribution. This is achieved by a data modeling of each frame in the sequence driven by the estimation of the instantaneous wavefront at the entrance pupil. Under suitable physical contraints, numerical convergence is guaranteed by an iteration scheme starting from a Compact MFBD (CMFBD) which provides a very robust initial guess which only employs a few frames. We describe the mathematics behind the process and report the high-resolution reconstruction of the spectroscopic binary {\alpha} And (16.3 mas separation) acquired with the precursor of SHARK-VIS, the upcoming high-contrast camera in the visible for the Large Binocular Telescope.