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Anisoplanatic imaging through turbulence using principal component analysis

: Baena-Gallé, Roberto; Gladysz, Szymon; Mateos, Javier

Postprint urn:nbn:de:0011-n-3706449 (957 KByte PDF)
MD5 Fingerprint: eaf50dced0169a8b4ddc3e8f226032c1
Created on: 24.10.2017

16th Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS 2015) : Maui, Hawaii, USA 15-18 September 2015
Red Hook, NY: Curran, 2016
ISBN: 978-1-5108-1541-4
Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) <16, 2015, Maui/Hawaii>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

The performance of optical systems is highly degraded by atmospheric turbulence when observing both vertically (e.g., astronomy, remote sensing) or horizontally (e.g. long-range surveillance). This problem can be partially alleviated using adaptive optics (AO) but only for small fields of view (FOV), described by the isoplanatic angle, for which the turbulence-induced aberrations can be considered constant. Additionally, this problem can also be tackled using post-processing techniques such as deconvolution algorithms which take into account the variability of the point spread function (PSF) in anisoplanatic conditions.
Variability of the PSF across the field of view in anisoplanatic imagery can be described using principal component analysis. Then, a certain number of variable PSFs can be used to create new basis functions, called principal components (PC), which can be considered constant across the FOV and, therefore, potentially be used to perform global deconvolution. Our approach is tested on simulated, single-conjugate AO data.