An image fusion approach based on markov random fields

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 12 PART 2, 2011, Pages 5116-5127

  Xu, Min ^a,b   Chen, Hao ^c   Varshney, Pramod K ^a  

^a Syracuse University   (United States)

^b Enventure Global Technology   (United States)

^c Boise State University   (United States)

Author keywords

Markov random field; multi resolution decomposition; multispectral image fusion

Indexed keywords

CONTEXTUAL CONSTRAINTS; FUSION ALGORITHMS; FUSION MODEL; FUSION PERFORMANCE; IMAGE FUSION APPROACH; IMAGE PROCESSING APPLICATIONS; MARKOV RANDOM FIELD MODELS; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI CRITERIA; MODEL IMAGES; MULTI RESOLUTION DECOMPOSITION; MULTI-SCALE DECOMPOSITION; MULTISPECTRAL IMAGES; REMOTE SENSING IMAGES;

ALGORITHMS; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; REMOTE SENSING;

IMAGE FUSION;

ALGORITHM; IMAGE PROCESSING; MARKOV CHAIN; REMOTE SENSING;

EID: 82155167667     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2158607     Document Type: Article

References (35)

1. R. S. Blum, "On multisensor image fusion performance limits from an estimation theory perspective," Inf. Fusion, vol. 7, no. 3, pp. 250-263, Sep. 2006. (Pubitemid 43832196)
2. Z. Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, "A comparative analysis of image fusion methods," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1391-1402, Jun. 2005. (Pubitemid 40811947)
3. C. Thomas, T. Ranchin, L. Wald, and J. Chanussot, "Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1301-1312, May 2008.
4. C. Pohl and J. van Genderen, "Multisensor image fusion in remote sensing: Concepts, methods, and applications," Int. J. Remote Sens., vol. 19, no. 5, pp. 823-854, 1998.
5. P. K. Varshney, B. Kumar, M. Xu, A. Drozd, and I. Kasperovich, "Image registration: A tutorial," in Proc. NATO ASI, Albena, Bulgaria, 2005.
6. Z. Zhang and R. S. Blum, "A categorization of multiscale- decompositionbased image fusion schemes with a performance study for a digital camera application," Proc. IEEE, vol. 87, no. 8, pp. 1315-1326, Aug. 1999.
7. R. K. Sharma, T. K. Leen, and M. Pavel, "Probabilistic image sensor fusion," in Proc. Adv. Neural Inf. Process. Syst. 11, 1999, pp. 824-830.
8. H.-M. Chen, S. Lee, R. Rao, M.-A. Slamani, and P. Varshney, "Imaging for concealed weapon detection: A tutorial overview of development in imaging sensors and processing," IEEE Signal Process. Mag., vol. 22, no. 2, pp. 52-61, Mar. 2005.
9. Y. Zhang, S. De Backer, and P. Scheunders, "Noise-resistant waveletbased Bayesian fusion of multispectral and hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3834-3843, Nov. 2009.
10. P. Burt and R. Kolczynski, "Enhanced image capture through fusion," in Proc. 4th Int. Conf. Comput. Vis., 1993, pp. 173-182. (Pubitemid 23667855)
11. J. Yang and R. Blum, "A statistical signal processing approach to image fusion for concealed weapon detection," in Proc. IEEE Int. Conf. Image Process., 2002, pp. 513-516.
12. A. Lozci, A. Achim, D. Bull, and N. Canagarajah, "Statistical image fusion with generalized Gaussian and Alpha-Stable distributions," in Proc. 15th Int. Conf. Digital Signal Process., 2007, pp. 268-271.
13. E. Lallier and M. Farooq, "A real time pixel-level based image fusion via adaptive weight averaging," in Proc. 3rd Int. Conf. Inf. Fusion, 2000, pp. WEC3/3-WEC313.
14. S. Z. Li, Markov Random Field Modeling in Computer Vision. New York: Spinger-Verlag, 2001.
15. M. Joshi and A. Jalobeanu, "MAP estimation for multiresolution fusion in remotely sensed images using an IGMRF prior model," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 3, pp. 1245-1255, Mar. 2010.
16. T. Kasetkasem and P. Varshney, "An image change detection algorithm based on Markov random field models," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 8, pp. 1815-1823, Aug. 2002. (Pubitemid 35170401)
17. Z. Tu and S. Zhu, "Image segmentation by data-driven Markov chain Monte Carlo," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 657-673, May 2002. (Pubitemid 34550432)
18. H. Chen, "Mutual information based image registration with applications," Ph.D. dissertation, Syracuse Univ., Syracuse, NY, May, 2002.
19. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," in Readings in Uncertain Reasoning. San Francisco, CA: Morgan Kaufmann, 1990, pp. 452-472.
20. L. Bedini, A. Tonazzini, and S. Minutoli, "Unsupervised edge-preserving image restoration via a saddle point approximation," Image Vis. Comput., vol. 17, no. 11, pp. 779-793, Sep. 1999. (Pubitemid 30500056)
21. D. Kundur, D. Hatzinakos, and H. Leung, "Robust classification of blurred imagery," IEEE Trans. Image Process., vol. 9, no. 2, pp. 243-255, Feb. 2000. (Pubitemid 30557216)
22. W. Wright, "Fast image fusion with a Markov random field," in Proc. 7th Int. Conf. Image Process. Appl., 1999, pp. 557-561. (Pubitemid 30503195)
23. R. S. Blum, "Robust image fusion using a statistical signal processing approach," Inf. Fusion, vol. 6, no. 2, pp. 119-128, Jun. 2005. (Pubitemid 40206024)
24. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ: Prentice-Hall, 1993.
25. Y. C. Eldar, A. Beck, and M. Teboulle, "Bounded error estimation: A Chebyshev center approach," in Proc. 2nd IEEE Int. Workshop Comput. Adv. Multi-Sensor Adapt. Process., 2007, pp. 205-208.
26. S. Lakshmanan and H. Derin, "Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 8, pp. 799-813, Aug. 1989.
27. V. Kolmogorov and R. Zabin, "What energy functions can be minimized via graph cuts?" IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 2, pp. 147-159, Feb. 2004.
28. F. Heitz, "Restriction of a Markov random field on a graph and multiresolution statistical image modeling," IEEE Trans. Inf. Theory, vol. 42, no. 1, pp. 180-190, Jan. 1996.
29. P. Bremaud, Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer-Verlag, 1999.
30. H. Derin and H. Elliott, "Modeling and segmentation of noisy and textured images using Gibbs random fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-9, no. 1, pp. 39-55, Jan. 1987. (Pubitemid 17534848)
31. T. Kasetkasem, "Image analysis methods based on Markov random field models," Ph.D. dissertation, Syracuse Univ., Syracuse, NY, Dec., 2002.
32. S. S. Saquib, C. A. Bouman, and K. Sauer, "ML parameter estimation for Markov random fields, with applications to Bayesian tomography," IEEE Trans. Image Process., vol. 7, no. 7, pp. 1029-1044, Jul. 1998. (Pubitemid 128745416)
33. J. Besag, "On the statistical analysis of dirty pictures," J. R. Stat. Soc., vol. 48, no. 3, pp. 259-302, 1986.
34. S. Geman and C. Graffigne, "Markov random field image models and their application to computer vision," in Proc. Int. Congr. Mathematicians, 1986, pp. 1496-1517.
35. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2008.