Image Fusion Using Tensor Decomposition and Coefficient Combining Scheme

Image Fusion Using Tensor Decomposition and Coefficient Combining Scheme

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)
© 2014 by IJETT Journal
Volume-15 Number-9
Year of Publication : 2014
Authors : Mugdha S. Rane , Prof. Dr. D. S. Bormane
  10.14445/22315381/IJETT-V15P286

Citation 

Mugdha S. Rane , Prof. Dr. D. S. Bormane. "Image Fusion Using Tensor Decomposition and Coefficient Combining Scheme", International Journal of Engineering Trends and Technology (IJETT), V15(9),453-459 Sep 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

In this paper, an effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on higher order singular value decomposition (HOSVD). Since image fusion depends on local information of source images, the proposed algorithm simply groups together similar patches of source images to constitute the fused image by processing the divided 3D stack rather than the whole tensor. Then it computes the sum of absolute values of the coefficients (SAVC) from HOSVD of sub-tensors for activity-level measurement to evaluate the quality of the related image patch; and a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative fusion approach for multi-modal and multi-focus images.

References

[1] A. A. Goshtasby and S. Nikolov, “Image fusion: Advances in the state of the art,” Inf. Fusion, vol. 8, no. 2, pp. 114–118, Apr. 2007.
[2] G. Bergqvist and E. G. Larsson, “The higher-order singular value decomposition: Theory and application,” IEEE Signal Process. Mag., vol. 27, no. 3, pp. 151–154, May 2010.
[3] E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. Qgden, “Pyramid methods in signal processing,” RCA Eng., vol. 29, no. 6, pp. 33–41, Nov./Dec. 1984.
[4] H. Yesou, Y. Besnus, and J. Rolet, “Extraction of spectral information from Landsat TM data and merger with SPOT panchromatic imagery— A contribution to the study of geological structures,” ISPRS J. Photogramm. Remote Sens., vol. 48, no. 5, pp. 23–36, Oct. 1993.
[5] H. Li, S. Manjunath, and S. Mitra, “Multi sensor image fusion using the wavelet transform,” Graph. Models Image Process., vol. 57, no. 3, pp. 235–245, May 1995.
[6] J. Tang, “A contrast based image fusion technique in the DCT domain,” Digit. Signal Process., vol. 14, no. 3, pp. 218–226, May 2004.
[7] D. Looney and D. Mandic, “Multiscale image fusion using complex extensions of EMD,” IEEE Trans. Signal Process., vol. 57, no. 4, pp. 1626–1630, Apr. 2009.
[8] J. Liang, Y. He, D. Liu, and X. Zeng, “Image fusion using higher order singular value decomposition,” IEEE Trans. Image Process., vol. 21, no. 5, May 2012.
[9] L. De Lathauwer, B. De Moor, and J. Vandewalle, “A multilinear singular value decomposition,” SIAM J. Matrix Anal. Appl., vol. 21, no. 4, pp. 1253–1278, Mar.–May 2000.
[10] R. Costantini, L. Sbaiz, and S. Susstrunk, “Higher order SVD analysis for dynamic texture synthesis,” IEEE Trans. Image Process., vol. 17, no. 1, pp. 42–52, Jan. 2008.
[11] C. S. Xydeas and V. Petrovic, “Objective image fusion performance measure,” Electron. Lett., vol. 36, no. 4, pp. 308–309, Feb. 2000.
[12] Mohammed Hossny, Saeid Nahavandi, Douglas Creighton, Asim Bhatti and Marwa Hassan, “Image Fusion Metrics: Evolution in a Nutshell”, pp. 443-450, IEEE 2013.
[13] Z.Wang and A. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81–84, Mar. 2002.
[14] M. Kumar and S. Dass, “A total variation-based algorithm for pixel level image fusion,” IEEE Trans. Image Process., vol. 18, no. 9, Sep. 2009.
[15] Hongbo Wu, YanqiuXing, “Pixel-based Image Fusion Using Wavelet Transform for SPOT and ETM+ Image”, IEEE International Conference on Image Processing, pp. 936-940, IEEE 2010.
[16] Hugo R. Albuquerque, Tsang Ing Ren and George D. C. Cavalcanti, “Image Fusion Combining Frequency Domain Techniques Based on Focus”, 24th International Conference on Tools with Artificial Intelligence, pp. 757-762, IEEE 2012.
[17] Shutao Li, Member, IEEE, Xudong Kang, Student Member, IEEE, and Jianwen Hu, “Image Fusion with Guided Filtering”, IEEE Trans. on Image Processing Vol. 22, No. 7, July 2013.
[18] M. Haardt, F. Roemer, and G. Del Galdo, “Higher-order SVD-based subspace estimation to improve the parameter estimation accuracy in multidimensional harmonic retrieval problems,” IEEE Trans. Signal Process., vol. 56, no. 7, pt. 2, pp. 3198–3213, Jul. 2008.
[19] Ajit Rajwade, Student Member, IEEE, Anand Rangarajan, Member, IEEE, and Arunava Banerjee, Member, IEEE, “Image Denoising Using the HOSVD”, IEEE Trans. on Pattern Analysis and Machine Intelligence Vol. 35, No. 4, April 2013.
[20] R. Shen, I. Cheng, J. Shi, and A. Basu, “Generalized random walks for fusion of multi-exposure images,” IEEE Trans. Image Process., vol. 20, no. 12, Dec. 2011.
[21] Brandon Miles, Ismail Ben Ayed, Member, IEEE, Max W. K. Law, Greg Garvin, Aaron Fenster, Senior Member, IEEE, and Shuo Li, “Spine Image Fusion Via Graph Cuts”, IEEE Trans. on Biomedical Engg, Vol. 60, No. 7, July 2013.

Keywords
Coefficient-combining strategy, singular value decomposition (SVD), image fusion, sigmoid function.