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 pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the 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).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
