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2019 | OriginalPaper | Buchkapitel

Multiple Image Deblurring with High Dynamic-Range Poisson Data

verfasst von : Marco Prato, Andrea La Camera, Carmelo Arcidiacono, Patrizia Boccacci, Mario Bertero

Erschienen in: Computational Methods for Inverse Problems in Imaging

Verlag: Springer International Publishing

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Abstract

An interesting problem arising in astronomical imaging is the reconstruction of an image with high dynamic range, for example a set of bright point sources superimposed to smooth structures. A few methods have been proposed for dealing with this problem and their performance is not always satisfactory. In this paper we propose a solution based on the representation, already proposed elsewhere, of the image as the sum of a pointwise component and a smooth one, with different regularization for the two components. Our approach is in the framework of Poisson data and to this purpose we need efficient deconvolution methods. Therefore, first we briefly describe the application of the Scaled Gradient Projection (SGP) method to the case of different regularization schemes and subsequently we propose how to apply these methods to the case of multiple image deconvolution of high-dynamic range images, with specific reference to the Fizeau interferometer LBTI of the Large Binocular Telescope (LBT). The efficacy of the proposed methods is illustrated both on simulated images and on real images, observed with LBTI, of the Jovian moon Io. The software is available at http://www.oasis.unimore.it/site/home/software.html.

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Vorheriges Kapitel The Hough Transform and the Impact of Chronic Leukemia on the Compact Bone Tissue from CT-Images Analysis

Nächstes Kapitel On the Segmentation of Astronomical Images via Level-Set Methods

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Titel: Multiple Image Deblurring with High Dynamic-Range Poisson Data
verfasst von: Marco Prato
Andrea La Camera
Carmelo Arcidiacono
Patrizia Boccacci
Mario Bertero
Verlag: Springer International Publishing
Buch: Computational Methods for Inverse Problems in Imaging
Print ISBN: 978-3-030-32881-8

Electronic ISBN: 978-3-030-32882-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32882-5_6

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