Proc. SPIE. 4792, Image Reconstruction from Incomplete Data II
KEYWORDS: Signal to noise ratio, Telescopes, Detection and tracking algorithms, Air contamination, Image restoration, Image quality, Space telescopes, Reconstruction algorithms, Modulation transfer functions, Filtering (signal processing)
Telescopes and imaging interferometers with sparsely filled apertures can be lighter weight and less expensive than conventional filled-aperture telescopes. However, their greatly reduced MTF’s cause significant blurring and loss of contrast in the collected imagery. Image reconstruction algorithms can correct the blurring completely when the signal-to-noise (SNR) is high, but only partially when the SNR is low. This paper compares both linear (Wiener) and nonlinear (iterative maximum likelihood) algorithms for image reconstruction under a variety of circumstances. These include high and low SNR, Gaussian noise and Poisson-noise dominated, and a variety of aperture configurations and degrees of sparsity. The quality metric employed to compare algorithms is image utility as quantified by the National Imagery Interpretability Rating Scale (NIIRS). On balance, a linear reconstruction algorithm with a power-law power-spectrum estimate performed best.
Imaging through volume turbulence gives rise to anisoplanatism (space-variant blur). The effects of volume turbulence on imaging are often modeled through the use of a sequence of phase screens distributed along the optical path. Wallner recently derived a prescription for the optimal functional form and location of multiple phase screens for use in simulating the effects of volume turbulence in infinite-range imaging geometries. We generalized Wallner's method to accommodate the finite range case and to have a more optimal functional form for the phase screens. These methods can also be used for designing a multi-conjugate AO system. Examples of optimal solutions are given for horizontal-path finite-range imaging cases.