An Image Fusion Approach Based on Fuzzy Gibbs Random Fields with Local Level Processing
Keywords:
Fuzzy Gibbs Random Field, local level processing, multiresolution decomposition, multispectral image fusionAbstract
Fuzzy Gibbs Random Field (FGRF) models with local level processing are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. In this paper, we investigate the problem of fusion of many types of images, such as multispectral image fusion, based on FGRF models with local level processing. This approach incorporates contextual constraints via FGRF models with local level processing into the fusion model. This algorithm is applicable to both multiscale decomposition (MD)-based image fusion and non-MD-based image fusion. Experimental results are provided to demonstrate the improvement in fusion performance achieved by our algorithms.
References
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.
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.
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.
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.
H. Derin and H. Elliott, “Modeling and segmentation of noisy and textured images using Fuzzy Gibbs Random Fields,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-9, no. 1, pp. 39–55, Jan. 1987.
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.
J. Besag, “On the statistical analysis of dirty pictures,” J. R. Stat. Soc., vol. 48, no. 3, pp. 259–302, 1986.
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.
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.
M. Xu, Hao Chen, and Pramod K. Varshney, "An Image Fusion Approach Based on Markov Random Fields," IEEE Transactions On Geoscience And Remote Sensing, vol. 49, no. 12, Dec. 2011.
P. Bremaud, Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues, New York: Springer-Verlag, 1999.
P. Burt and R. Kolczynski, “Enhanced image capture through fusion,” in Proc. 4th Int. Conf. Comput. Vis., 1993, pp. 173–182.
P. K. Varshney, B. Kumar, M. Xu, A. Drozd, and I. Kasperovich, “Image registration: A tutorial,” in Proc. NATO ASI, Albena, Bulgaria, 2005.
Peter J. Burt and Raymond J. Kolczynski, "Enhanced Image Capture Through Fusion," David Sarnoff Research Center, Princeton NJ, 08543-5300.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Upper Saddle River, NJ: Prentice-Hall, 2008.
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.
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.
S. Lakshmanan and H. Derin, “Simultaneous parameter estimation and segmentation of Fuzzy Gibbs Random Fields using simulated annealing,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 8, pp. 799–813, Aug. 1989.
S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Upper Saddle River, NJ: Prentice-Hall, 1993.
S. Z. Li, Markov Random Field Modeling in Computer Vision, New York: Springer-Verlag, 2001.
T. Kasetkasem, “Image analysis methods based on Markov random field models,” Ph.D. dissertation, Syracuse Univ., Syracuse, NY, Dec. 2002.
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.
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.
Y. Zhang, S. De Backer, and P. Scheunders, “Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3834–3843, Nov. 2009.
Yifan Zhang, Steve De Backer, and Paul Scheunders, "Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images," IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 47, no. 11, Nov. 2009.
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.
Z. Zhang and R. S. Blum, “A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application,” Proc. IEEE, vol. 87, no. 8, pp. 1315–1326, Aug. 1999.
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