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

School of Electrical and 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

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@article{0b0b3c093aa94035b953463bfca9146f,

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.",

author = "Ravi Krishnamurthy and Woods, \{John W.\} and Francos, \{Joseph M.\}",

year = "1996",

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T1 - Adaptive restoration of textured images with mixed spectra

AU - Krishnamurthy, Ravi

AU - Woods, John W.

AU - Francos, Joseph M.

PY - 1996/12/1

Y1 - 1996/12/1

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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U2 - 10.1109/83.491341

DO - 10.1109/83.491341

M3 - Article

AN - SCOPUS:0006804725

SN - 1057-7149

VL - 5

SP - 648

EP - 652

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

IS - 4

ER -

Adaptive restoration of textured images with mixed spectra

Abstract

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