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Proceedings Paper

Primary and secondary superresolution: degrees of freedom versus Fourier extrapolation
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Paper Abstract

Superresolution of images by data inversion is defined as extrapolating measured Fourier data into regions of Fourier space where no measurements have been taken. This type of superresolution can only occur by data inversion. There exist two camps of thought regarding the efficacy of this type of superresolution: the first is that meaningful superresolution is unachievable due to signal-to-noise limitations, and the second is that meaningful superresolution is possible. Here we present a framework for describing superresolution in a way that accommodates both points of view. In particular, we define the twin concepts of primary and secondary superresolution and show that the first camp is referring to primary superresolution while the second group is referring to secondary superresolution. We discuss the implications of both types of superresolution on the ability of data inversion to achieve meaningful superresolution.

Paper Details

Date Published: 22 October 2004
PDF: 9 pages
Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.555938
Show Author Affiliations
Charles L. Matson, Air Force Research Lab. (United States)
David W. Tyler, Optical Sciences Ctr./Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 5562:
Image Reconstruction from Incomplete Data III
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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