Signal extrapolation based on wavelet representation

Xiang Gen Xia; C. C.Jay Kuo; Zhen Zhang

doi:10.1117/12.162086

Signal extrapolation based on wavelet representation

Xiang Gen Xia, C. C.Jay Kuo, Zhen Zhang

University of Southern California

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

The Papoulis-Gerchberg (PG) algorithm is well known for band-limited signal extrapolation. We consider the generalization of the PG algorithm to signals in the wavelet subspaces in this research. The uniqueness of the extrapolation for continuous-time signals is examined, and sufficient conditions on signals and wavelet bases for the generalized PG (GPG) algorithm to converge are given. We also propose a discrete GPG algorithm for discrete-time signal extrapolation, and investigate its convergence. Numerical examples are given to illustrate the performance of the discrete GPG algorithm.

Original language	English
Pages (from-to)	33-44
Number of pages	12
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2034
DOIs	https://doi.org/10.1117/12.162086
Publication status	Published - 1 Nov 1993
Externally published	Yes
Event	Mathematical Imaging: Wavelet Applications in Signal and Image Processing 1993 - San Diego, United States Duration: 11 Jul 1993 → 16 Jul 1993

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title = "Signal extrapolation based on wavelet representation",

abstract = "The Papoulis-Gerchberg (PG) algorithm is well known for band-limited signal extrapolation. We consider the generalization of the PG algorithm to signals in the wavelet subspaces in this research. The uniqueness of the extrapolation for continuous-time signals is examined, and sufficient conditions on signals and wavelet bases for the generalized PG (GPG) algorithm to converge are given. We also propose a discrete GPG algorithm for discrete-time signal extrapolation, and investigate its convergence. Numerical examples are given to illustrate the performance of the discrete GPG algorithm.",

author = "Xia, {Xiang Gen} and Kuo, {C. C.Jay} and Zhen Zhang",

note = "Publisher Copyright: {\textcopyright} 1993 SPIE. All rights reserved.; Mathematical Imaging: Wavelet Applications in Signal and Image Processing 1993 ; Conference date: 11-07-1993 Through 16-07-1993",

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T1 - Signal extrapolation based on wavelet representation

AU - Xia, Xiang Gen

AU - Kuo, C. C.Jay

AU - Zhang, Zhen

PY - 1993/11/1

Y1 - 1993/11/1

N2 - The Papoulis-Gerchberg (PG) algorithm is well known for band-limited signal extrapolation. We consider the generalization of the PG algorithm to signals in the wavelet subspaces in this research. The uniqueness of the extrapolation for continuous-time signals is examined, and sufficient conditions on signals and wavelet bases for the generalized PG (GPG) algorithm to converge are given. We also propose a discrete GPG algorithm for discrete-time signal extrapolation, and investigate its convergence. Numerical examples are given to illustrate the performance of the discrete GPG algorithm.

AB - The Papoulis-Gerchberg (PG) algorithm is well known for band-limited signal extrapolation. We consider the generalization of the PG algorithm to signals in the wavelet subspaces in this research. The uniqueness of the extrapolation for continuous-time signals is examined, and sufficient conditions on signals and wavelet bases for the generalized PG (GPG) algorithm to converge are given. We also propose a discrete GPG algorithm for discrete-time signal extrapolation, and investigate its convergence. Numerical examples are given to illustrate the performance of the discrete GPG algorithm.

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U2 - 10.1117/12.162086

DO - 10.1117/12.162086

M3 - Conference article

AN - SCOPUS:2342640560

SN - 0277-786X

VL - 2034

SP - 33

EP - 44

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Mathematical Imaging: Wavelet Applications in Signal and Image Processing 1993

Y2 - 11 July 1993 through 16 July 1993

ER -

Signal extrapolation based on wavelet representation

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