Adaptive restoration of textured images with mixed spectra

Ravi Krishnamurthy; John W. Woods; Joseph M. Francos

doi:10.1109/83.491341

Adaptive restoration of textured images with mixed spectra

Ravi Krishnamurthy, John W. Woods, Joseph M. Francos

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.

Original language	English
Pages (from-to)	648-652
Number of pages	5
Journal	IEEE Transactions on Image Processing
Volume	5
Issue number	4
DOIs	https://doi.org/10.1109/83.491341
State	Published - 1 Dec 1996

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/83.491341

Cite this

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title = "Adaptive restoration of textured images with mixed spectra",

abstract = "We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.",

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AU - Woods, John W.

AU - Francos, Joseph M.

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N2 - We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.

AB - We consider the adaptive restoration of inhomogeneous textured images, where the individual regions are modeled using a Wold-like decomposition. A generalized Wiener filter is developed to accommodate mixed spectra, and unsupervised restoration is achieved by using the expectation-maximization (EM) algorithm to estimate the degradation parameters. This algorithm yields superior results when compared with supervised Wiener filtering using autoregressive (AR) image models.

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M3 - Article

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JO - IEEE Transactions on Image Processing

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Adaptive restoration of textured images with mixed spectra

Abstract

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