Bilateral Markov mesh random field and its application to image restoration

被引：4

作者：

Yousefi, S. ^{[1
]}

Kehtarnavaz, N. ^{[1
]}

Cao, Y. ^{[2
]}

Razlighi, Q. R. ^{[3
]}

机构：

[1] Univ Texas Dallas, Dept Elect Engn, Richardson, TX 75083 USA

[2] Univ Texas Dallas, Dept Math Sci, Richardson, TX 75083 USA

[3] Columbia Univ, Dept Mol Imaging & Neuropathol, New York, NY 10027 USA

来源：

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | 2012年 / 23卷 / 07期

关键词：

Markov random fields; Bilateral Markov mesh random field; Image modeling; Image restoration; Image processing; Texture analysis; Causal Markov random field; Stochastic image analysis; SEGMENTATION;

D O I：

10.1016/j.jvcir.2012.06.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces bilateral Markov mesh random field to overcome the shortcomings of the conventional Markov random fields in image modeling. These shortcomings consist of (a) the computational intractability of such fields when expressing the image probability function in the form of the Gibbs distribution function, and (b) the formulation of the image probability function via the product of low-dimensional densities at the expense of obtaining non-symmetrical image models. The properties of bilateral Markov mesh random field are presented and used to derive an image model to address the above shortcomings. As an application, a framework for image restoration is then provided. Restoration results based on this new bilateral Markov mesh random field are compared to the conventional fields to demonstrate its effectiveness. Published by Elsevier Inc.

引用

页码：1051 / 1059

页数：9

共 25 条

[1] CLASSIFICATION OF BINARY RANDOM PATTERNS [J].

ABEND, K ;

HARLEY, TJ ;

KANAL, LN .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1965, 11 (04) :538-544

[2]

BESAG J, 1974, J ROY STAT SOC B MET, V36, P192

[3]

BESAG J, 1986, J R STAT SOC B, V48, P259

[4]

Boyok Y., 2001, IEEE T PATTERN ANAL, V23, P1222

[5] Stationary Markov random fields on a finite rectangular lattice [J].

Champagnat, F ;

Idier, J ;

Goussard, Y .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1998, 44 (07) :2901-2916

[6] Binary Markov Mesh Models and Symmetric Markov Random Fields: Some Results on their Equivalence [J].

Noel Cressie ;

Craig Liu .

Methodology And Computing In Applied Probability, 2001, 3 (1) :5-34

[7] BAYES SMOOTHING ALGORITHMS FOR SEGMENTATION OF BINARY IMAGES MODELED BY MARKOV RANDOM-FIELDS [J].

DERIN, H ;

ELLIOTT, H ;

CRISTI, R ;

GEMAN, D .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (06) :707-720

[8] MODELING AND SEGMENTATION OF NOISY AND TEXTURED IMAGES USING GIBBS RANDOM-FIELDS [J].

DERIN, H ;

ELLIOTT, H .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (01) :39-55

[9]

Devijver P. A., 1998, PATTERN RECOGN, V301, P131

[10] STOCHASTIC RELAXATION, GIBBS DISTRIBUTIONS, AND THE BAYESIAN RESTORATION OF IMAGES [J].

GEMAN, S ;

GEMAN, D .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1984, 6 (06) :721-741

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