Finite rate of innovation with non-uniform samples

In this paper, we investigate the problem of retrieving the innovation parameters (time and amplitude) of a stream of Diracs from non-uniform samples taken with a novel kernel (a hyperbolic secant). We devise a non-iterative, exact algorithm that allows perfect reconstruction of 2K innovations from...

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Bibliographic Details
Published in2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC) pp. 369 - 372
Main Authors Xiaoyao Wei, Blu, Thierry, Dragotti, P-L
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2012
Subjects
Cramér-Rao Bounds
finite rate of innovation
hyperbolic secant function
Image reconstruction
Kernel
Mathematical model
Noise measurement
non-uniform
PSNR
Signal sampling
Technological innovation
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Summary:In this paper, we investigate the problem of retrieving the innovation parameters (time and amplitude) of a stream of Diracs from non-uniform samples taken with a novel kernel (a hyperbolic secant). We devise a non-iterative, exact algorithm that allows perfect reconstruction of 2K innovations from as few as 2K non-uniform samples. We also investigate noise issues and compute the Cramér-Rao lower bounds for this problem. A simple total least-squares extension of the algorithm proves to be efficient in reconstructing the location of a single Dirac from noisy measurements.
ISBN:9781467321921
1467321923
DOI:10.1109/ICSPCC.2012.6335674