Issue |
J. Eur. Opt. Soc.-Rapid Publ.
Volume 8, 2013
|
|
---|---|---|
Article Number | 13072 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.2971/jeos.2013.13072 | |
Published online | 29 October 2013 |
Regular papers
Semi-Huber quadratic function and comparative study of some MRFs for Bayesian image restoration
1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Antiguo Camino a la Bufa No. 1, Col. Centro. C. P. 98000, Zacatecas, Zac., México
2
Instituto Tecnológico Superior de Fresnillo, Av. Tecnológico No. 2000 Col. Solidaridad, C.P. 99010 Fresnillo, Zac., México
3
École Supérieure d’Électricité (SUPELEC), Plateau de Moulon, 3 rue Joliot Curie, 91192 Gif-sur-Yvette Cedex, France
Received:
10
July
2013
Revised:
13
October
2013
The present work introduces an alternative method to deal with digital image restoration into a Bayesian framework, particularly, the use of a new half-quadratic function is proposed which performance is satisfactory compared with respect to some other functions in existing literature. The bayesian methodology is based on the prior knowledge of some information that allows an efficient modelling of the image acquisition process. The edge preservation of objects into the image while smoothing noise is necessary in an adequate model. Thus, we use a convexity criteria given by a semi-Huber function to obtain adequate weighting of the cost functions (half-quadratic) to be minimized. The principal objective when using Bayesian methods based on the Markov Random Fields (MRF) in the context of image processing is to eliminate those effects caused by the excessive smoothness on the reconstruction process of image which are rich in contours or edges. A comparison between the new introduced scheme and other three existing schemes, for the cases of noise filtering and image deblurring, is presented. This collection of implemented methods is inspired of course on the use of MRFs such as the semi-Huber, the generalized Gaussian, the Welch, and Tukey potential functions with granularity control. The obtained results showed a satisfactory performance and the effectiveness of the proposed estimator with respect to other three estimators.
Key words: Digital image processing / image filtering / image deblurring / Markov random fields (MRF) / half-quadratic functions
© The Author(s) 2013. All rights reserved.
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