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Radioastronomical Image Reconstruction with Regularized Least Squares

Citation Author(s):
Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen
Submitted by:
Shahrzad Naghibzadeh
Last updated:
23 March 2016 - 6:55pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters:
Shahrzad Naghibzadeh
Paper Code:
3579
Categories:

Abstract

Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the ``dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

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