Superresolution with compound Markov random fields via the variational EM algorithm

Neural Networks

Volumn 22, Issue 7, 2009, Pages 1025-1034

  Kanemura, Atsunori ^a   Maeda, Shin ichi ^a   Ishii, Shin ^a,b  

^a KYOTO UNIVERSITY   (Japan)

^b NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

Bayesian marginalization; Compound Markov random fields; Edge preservation; Image registration; Image superresolution; Variational EM algorithms

Indexed keywords

BAYESIAN MARGINALIZATION; COMPOUND MARKOV RANDOM FIELDS; EDGE PRESERVATION; IMAGE SUPERRESOLUTION; VARIATIONAL EM ALGORITHMS;

ALGORITHMS; BAYESIAN NETWORKS; HIDDEN MARKOV MODELS; IMAGE ACQUISITION; IMAGE RECONSTRUCTION; IMAGE REGISTRATION; IMAGE SEGMENTATION; MAXIMUM LIKELIHOOD ESTIMATION; MEDICAL IMAGING; OPTICAL RESOLVING POWER; PARAMETER ESTIMATION; SECURITY OF DATA; STRUCTURAL FRAMES;

EDGE DETECTION;

ALGORITHM; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; HIDDEN MARKOV MODEL; IMAGE PROCESSING; IMAGE QUALITY; IMAGE RECONSTRUCTION; MATHEMATICAL COMPUTING; NORMAL DISTRIBUTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

ALGORITHMS; BAYES THEOREM; DIAGNOSTIC IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; NONLINEAR DYNAMICS;

EID: 69449085531     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2008.12.005     Document Type: Article

References (35)

1. Attias H. Independent factor analysis. Neural Computation 11 4 (1999) 803-851
2. Bishop C.M. Neural networks for pattern recognition (1995), Oxford University Press, New York
3. Bishop C.M. Pattern recognition and machine learning (2006), Springer, New York
4. Borman, S., & Stevenson, R. L. (1998). Spatial resolution enhancement of low-resolution image sequences: A comprehensive review with directions for future research. Tech. rep. Department of Electrical Engineering, University of Notre Dame
5. In: Chellappa R., and Jain A. (Eds). Markov random fields: Theory and application (1991), Academic Press, San Diego, CA
6. Elad M., and Feuer A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 6 12 (1997) 1646-1658
7. Farsiu S., Robinson D., Elad M., and Milanfar P. Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 14 2 (2004) 47-57
8. Farsiu S., Robinson D., Elad M., and Milanfar P. Fast and robust multiframe super resolution. IEEE Transactions on Image Processing 13 10 (2004) 1327-1344
9. Freeman W.T., Jones T.R., and Pasztor E.C. Example-based super-resolution. IEEE Computer Graphics Applications 22 2 (2002) 56-65
10. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-6 6 (1984) 721-741
11. Ghahramani Z., and Jordan M.I. Factorial hidden Markov models. Machine Learning 29 2-3 (1997) 245-273
12. Hardie R.C., Barnard K.J., and Armstrong E.E. Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Transactions on Image Processing 6 12 (1997) 1621-1633
13. Hirayama J., Yoshimoto J., and Ishii S. Bayesian representation learning in the cortex regulated by acetylcholine. Neural Networks 17 10 (2004) 1391-1400
14. Humblot F., and Mohammad-Djafari A. Super-resolution using hidden Markov model and Bayesian detection estimation framework. EURASIP Journal of Applied Signal Processing (2006) 1-16
15. Jaakkola T.S., and Jordan M.I. Bayesian parameter estimation via variational methods. Statistics and Computing 10 1 (2000) 25-37
16. Jeng F.-C., and Woods J.W. Simulated annealing in compound Gaussian random fields. IEEE Transactions on Information Theory 36 1 (1990) 94-107
17. Jeng F.-C., and Woods J.W. Compound Gauss-Markov random fields for image estimation. IEEE Transactions on Signal Processing 39 3 (1991) 683-697
18. Jordan M.I., Ghahramani Z., Jaakkola T.S., and Saul L.K. An introduction to variational methods for graphical models. Machine Learning 37 2 (1999) 183-233
19. Kim S.P., Bose N.K., and Valenzuela H.M. Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Transactions on Acoustics, Speech, and Signal Processing 38 6 (1990) 1013-1027
20. Likas A.C., and Galatsanos N.P. A variational approach for Bayesian blind image deconvolution. IEEE Transactions on Image Processing 52 8 (2004) 2222-2233
21. Molina R., Mateos J., Katsaggelos A.K., and Vega M. Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Transactions on Image Processing 12 12 (2003) 1642-1654
22. Molina R., Mateos J., and Katsaggelos A.K. Blind deconvolution using a variational approach to parameter, image, and blur estimation. IEEE Transactions on Image Processing 15 12 (2006) 3715-3727
23. Molina R., Vega M., Abad J., and Katsaggelos A.K. Parameter estimation in Bayesian high-resolution image reconstruction with multisensors. IEEE Transactions on Image Processing 12 12 (2003) 1655-1667
24. Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Tech. rep. no. CRG-TR-93-1. Department of Computer Science, University of Toronto
25. Neal R.M., and Hinton G.E. A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan M.I. (Ed). Learning in graphical models (1998), Kluwer Academic Press, Dordrecht
26. Park S.C., Park M.K., and Kang M.G. Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine 20 3 (2003) 21-36
27. Rajan D., and Chaudhuri S. An MRF-based approach to generation of super-resolution images from blurred observations. Journal of Mathematical Imaging and Vision 16 (2002) 5-15
28. Schultz R.R., and Stevenson R.L. Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 5 6 (1996) 996-1011
29. Tanaka K. Statistical-mechanical approach to image processing. Journal of Physics A: Mathematical and General 35 37 (2002) R81-R150
30. Tipping M.E., and Bishop C.M. Bayesian image super-resolution. In: Becker S., Thrun T., and Obermayer K. (Eds). Advances in neural information processing systems Vol. 15 (2003), MIT Press 1279-1286
31. Tsai R.Y., and Huang T.S. Multiframe image restoration and registration. Advances in computer vision and image processing Vol. 1 (1984), JAI Press, Greenwich, CT 317-339
32. Wainwright M.J., and Jordan M.I. A variational principle for graphical models. In: Haykin S., Principe J., Sejnowski T., and McWhirter J. (Eds). New directions in statistical signal processing (2005), MIT Press, Cambridge, MA (Chap. 11)
33. Willsky A.S. Multiresolution Markov models for signal and image processing. Proceedings of the IEEE 90 8 (2002) 1396-1458
34. Winkler G. Image analysis, random fields, and Markov chain Monte Carlo methods. 2nd ed. (2003), Springer
35. Woods N.A., Galatsanos N.P., and Katsaggelos A.K. Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images. IEEE Transactions on Image Processing 15 1 (2006) 201-213