TY - JOUR
T1 - Deterministic estimation of the bispectrum and its application to image restoration
AU - Kang, Moon Gi
AU - Katsaggelos, Aggelos K.
N1 - Publisher Copyright:
© 2015 European Signal Processing Conference, EUSIPCO. All rights reserved.
PY - 2015
Y1 - 2015
N2 - While the bispectrum has desirable properties in itself and therefore has a lot of potential to be applied to image restoration, few real-world application results have appeared in the literature. The major problem with this is the difficulty in realizing the expectation operator, due to the lack of realizations. In this paper, the true bispectrum is defined as the expectation of the sample bispectrum, which is the Fourier representation of the triple correlation given one realization. The characteristics of sample bispectrum are analyzed and a way to obtain an estimate of the true bispectrum without stochastic expectation, using the generalized theory of weighted regularization is shown.
AB - While the bispectrum has desirable properties in itself and therefore has a lot of potential to be applied to image restoration, few real-world application results have appeared in the literature. The major problem with this is the difficulty in realizing the expectation operator, due to the lack of realizations. In this paper, the true bispectrum is defined as the expectation of the sample bispectrum, which is the Fourier representation of the triple correlation given one realization. The characteristics of sample bispectrum are analyzed and a way to obtain an estimate of the true bispectrum without stochastic expectation, using the generalized theory of weighted regularization is shown.
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M3 - Conference article
AN - SCOPUS:85113907207
SN - 2219-5491
JO - European Signal Processing Conference
JF - European Signal Processing Conference
T2 - 8th European Signal Processing Conference, EUSIPCO 1996
Y2 - 10 September 1996 through 13 September 1996
ER -