Affiliation:
1. Michigan Technological University
Abstract
Practical considerations such
as cost constrain the aperture size of conventional telescopes, which,
combined with atmospheric turbulence effects, even in the presence of
adaptive optics, limit achievable angular resolution. Sparse aperture
telescopes represent a viable alternative for achieving improved
angular resolution by combining light collected from small apertures
distributed over a wide spatial area either using amplitude
interferometry or a direct imaging approach to beam-combining. The
so-called densified hypertelescope imaging concept in particular
provides a methodology for direct image formation from large sparse
aperture arrays. The densification system suppresses wide-angle side
lobes and concentrates that energy in the center of the focal plane,
significantly improving the signal-to-noise ratio of the measurement.
Even with densification, an inevitable consequence of sparse aperture
sampling is that the point-spread function associated with the direct
image contains an additional structure not present in full aperture
imaging systems. Postdetection image reconstruction is performed here
to compute a high-fidelity estimate of the measured object in the
presence of noise. In this paper, we describe a penalized
least-squares object-estimation approach and compare the results with
the classical Richardson–Lucy deconvolution algorithm as it is applied
to hypertelescope image formation. The parameters of the algorithm are
selected based on a comprehensive simulation study using the structure
similarity metric to assess reconstruction performance. We find that
the penalized least-squares formulation with optimized parameters
provides significantly improved reconstructions compared with the
conventional Richardson–Lucy algorithm.
Funder
Intelligence Advanced Research Projects
Activity
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering