Parameter-Free Restoration Algorithms for Two Classes of Binary MRF Images Degraded by Flip-Flap Noises
Summary :
The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.10 pp.2022-2031
- Publication Date
- 1997/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Image Theory
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Bing ZHANG, Mehdi N. SHIRAZI, Hideki NODA, "Parameter-Free Restoration Algorithms for Two Classes of Binary MRF Images Degraded by Flip-Flap Noises" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 10, pp. 2022-2031, October 1997, doi: .
Abstract: The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_10_2022/_p
Copy
@ARTICLE{e80-a_10_2022,
author={Bing ZHANG, Mehdi N. SHIRAZI, Hideki NODA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Parameter-Free Restoration Algorithms for Two Classes of Binary MRF Images Degraded by Flip-Flap Noises},
year={1997},
volume={E80-A},
number={10},
pages={2022-2031},
abstract={The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Parameter-Free Restoration Algorithms for Two Classes of Binary MRF Images Degraded by Flip-Flap Noises
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2022
EP - 2031
AU - Bing ZHANG
AU - Mehdi N. SHIRAZI
AU - Hideki NODA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1997
AB - The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.
ER -
English
