A method for space-variant deblurring with application to adaptive optics imaging in astronomy
arXiv:1505.01684 · doi:10.1051/0004-6361/201525610
Abstract
Images from adaptive optics systems are generally affected by significant distortions of the point spread function (PSF) across the field of view, depending on the position of natural and artificial guide stars. Image reduction techniques circumventing or mitigating these effects are important tools to take full advantage of the scientific information encoded in AO images. The aim of this paper is to propose a method for the deblurring of the astronomical image, given a set of samples of the space-variant PSF. The method is based on a partitioning of the image domain into regions of isoplanatism and on applying suitable deconvolution methods with boundary effects correction to each region. The effectiveness of the boundary effects correction is proved. Moreover, the criterion for extending the disjoint sections to partially overlapping sections is validated. The method is applied to simulated images of a stellar system characterized by a spatially variable PSF. We obtain good photometric quality, and therefore good science quality, by performing aperture photometry on the deblurred images. The proposed method is implemented in IDL in the Software Package "Patch", which is available on http://www.airyproject.eu.
11 pages, 9 figures, 7 tables, accepted by A&A
References in corpus (5)
- The Dartmouth Stellar Evolution Database
- Analysis of isoplanatic high resolution stellar fields by Starfinder code
- On a new Near-Infrared method to estimate the absolute ages of star clusters: NGC3201 as a first test case
- Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes
- Deconvolution With a Spatially-Variant PSF
Cited by in corpus (4)
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- Stellar photometry with Multi Conjugate Adaptive Optics
- Efficient Algorithms for Convolutional Inverse Problems in Multidimensional Imaging