Abstract
In image fusion techniques based on joint sparse representation (JSR), the composite image is calculated from the fusion of features, which are represented with sparse coefficients. Orthogonal matching pursuit (OMP) and basis pursuit (BP) are the main candidates to estimate the coefficients. Previously OMP is utilized for the advantage of low complexity. However, noticeable errors occur when the dictionary of JSR cannot ensure the coefficients are sparse enough. Alternatively, BP is more robust than OMP in such cases (though suffered from larger complexity). Unfortunately, it has never been studied in image fusion tasks. In this paper, we investigate JSR based on BP for image fusion. The target is to verify that 1) to what extent can BP outperform OMP; and 2) what is the trade-off between BP and OMP. Finally, we conclude, in some cases, fusion with BP obviously outperforms the one with OMP under an affordable computational complexity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Li, H., Manjunath, B.S., Mitra, S.K.: Multisensor Image Fusion Using the Wavelet Transform. Graphical Models and Image Processing 57(3), 235–245 (1995)
Goshtasby, A.A., Nikolov, S.: Image Fusion: Advances in The State of The Art. Information Fusion 8(2), 114–118 (2007)
Stathaki, T.: Image Fusion: Algorithms and Applications. Elsevier, Oxford (2008)
Yin, H., Li, S.: Multimodal Image Fusion with Joint Sparsity Model. Optical Engineering (6) (2011)
Yu, N., Qiu, T.S., Bi, F., Wang, A.Q.: Image Features Extraction and Fusion Based on Joint Sparse Representation. IEEE J. Selected Topics in Signal Processing 5(5), 1074–1082 (2011)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic Decomposition by Basis Pursuit. SIAM Review 43(1), 129–159 (2001)
Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition. In: Proc. 27th Annu. Asilomar Conf., Signals, Systems, and Computers, Pacific Grove, CA, pp. 40–44 (1993)
Donoho, D.L., Elad, M., Temlyakov, V.N.: Stable Recovery of Spare Overcomplete Representations in the Presence of Noise. IEEE Trans. Information Theory 52(1) (2006)
Elad, M.: Towards Average Performance Analysis. In: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, pp. 137–152. Springer, New York (2010)
Duarte, M., Sarvotham, S., Baron, D., Wakin, M., Baraniuk, R.: Distributed Compressed Sensing of Jointly Sparse Signals. In: Proc. Asilomar Conf., Signals, Syst. Comput., Pacific Grove, pp. 1537–1541 (2005)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least Angle Regression. Annals of Statistics 32(2), 407–499 (2004)
Yang, B., Li, S.: Multifocus Image Fusion and Restoration With Sparse Representation. IEEE Trans. Instrum. Meas. 59(4) (2010)
Marial, J., Ponce, J., Sapiro, G.: Online Dictionary Learning for Sparse Coding. In: Proc. International Conference on Machine Learning, Montreal, Canada, pp. 689–696 (2009)
Online Resource for Research in Image Fusion, http://www.ImageFusion.org
Xydeas, C.S., Petrovic, V.: Objective Image Fusion Performance Measure. Electronics Letters 36(4), 308–309 (2000)
Sparse Modeling Software, http://spams-devel.gforge.inria.fr
Carper, T.W., Lillesand, T.M., Kiefer, R.W.: The Use of Intensity-Hue-Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data. Photogrammetric Engineering and Remote Sensing 56, 459–467 (1990)
Akerman, A.: Pyramid Techniques for Multisensor Fusion. In: Proceedings of SPIE, vol. 1828, pp. 124–131 (1992)
Chen, T., Zhang, J., Zhang, Y.: Remote Sensing Image Fusion Based on Ridgelet Transform. In: Proc. Geo. Remote Sens. IEEE Int. Symp., pp. 1150–1153 (2005)
Nencini, F., Garzelli, A., Baronti, S., Alparone, L.: Remote Sensing Image Fusion Using The Curvelet Transform. Information Fusion 8(2), 143–156 (2007)
Do, M.N., Vetterli, M.: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Trans. on Image Processing 14(12), 2091–2106 (2005)
Yang, B., Li, S.: Pixel-Level Image Fusion with Simultaneous Orthogonal Matching Pursuit. Information Fusion 13(1), 10–19 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yao, Y., Xin, X., Guo, P. (2012). OMP or BP? A Comparison Study of Image Fusion Based on Joint Sparse Representation. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-34500-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34499-2
Online ISBN: 978-3-642-34500-5
eBook Packages: Computer ScienceComputer Science (R0)