Adaptive Restoration of Degraded Binary MRF Images Using EM Method
Summary :
An adaptive restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov Random Field (MRF) and the nonadditive flip noises are assumed to be statistically independent and asymmetric. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.2 pp.259-268
- Publication Date
- 1993/02/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Computer Graphics and Pattern Recognition
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Tatsuya YAMAZAKI, Mehdi N.SHIRAZI, Hideki NODA, "Adaptive Restoration of Degraded Binary MRF Images Using EM Method" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 2, pp. 259-268, February 1993, doi: .
Abstract: An adaptive restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov Random Field (MRF) and the nonadditive flip noises are assumed to be statistically independent and asymmetric. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_2_259/_p
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@ARTICLE{e76-d_2_259,
author={Tatsuya YAMAZAKI, Mehdi N.SHIRAZI, Hideki NODA, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Restoration of Degraded Binary MRF Images Using EM Method},
year={1993},
volume={E76-D},
number={2},
pages={259-268},
abstract={An adaptive restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov Random Field (MRF) and the nonadditive flip noises are assumed to be statistically independent and asymmetric. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Adaptive Restoration of Degraded Binary MRF Images Using EM Method
T2 - IEICE TRANSACTIONS on Information
SP - 259
EP - 268
AU - Tatsuya YAMAZAKI
AU - Mehdi N.SHIRAZI
AU - Hideki NODA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1993
AB - An adaptive restoration algorithm is developed for binary images degraded nonadditively with flip noises. The true image is assumed to be a realization of a Markov Random Field (MRF) and the nonadditive flip noises are assumed to be statistically independent and asymmetric. Using the Expectation and Maximization (EM) method and approximating the Baum's auxiliary function, the degraded image is restored iteratively. The algorithm is implemented as follows. First, the unknown parameters and the true image are guessed or estimated roughly. Second, using the true image estimate, the Baum's auxiliary function is approximated and then the noise and MRF parameters are reestimated. To reestimate the MRF parameters the Maximum Pseudo-likelihood (MPL) method is used. Third, using the Iterated Conditional Modes (ICM) method, the true image is reestimated. The second and third steps are carried out iteratively until by some ad hoc criterion a critical point of EM algorithm is approximated. A number of simulation examples are presented which show the effectiveness of the algorithm and the parameter estimation procedures.
ER -
English
