Novel infrared and visible image fusion method based on independent component analysis

Abstract

The goal of infrared (IR) and visible image fusion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis (ICA) is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can depress the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fusion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the proposed method can provide better perception effect.

Keywords

image fusion / independent component analysis (ICA) / feature extraction / kurtosis

References

[1] Goshtasby A A, Nikolov S. Image fusion: Advances in the state of the art. Information Fusion, 2008, 8(2): 114−118

[2] Mahmood A, Tudor PM, Oxford W, Hansford R, Nelson J D B, Kingsbury N G, Katartzis A, Petrou M, Mitianoudis N, Stathaki T, Achim A, Bull D, Canagarajah N, Nikolov S, Loza A, and Cvejic N. Applied multi-dimensional fusion. The Computer Journal, 2007, 50(6): 660−673

[3] Sinha A, Chen H M, Danu D G, Kirubarajan T, Farooq M. Estimation and decision fusion: a survey. Neurocomputing, 2008, 71(13−15): 2650−2656

[4] Piella G. A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 2003, 4: 259−280

[5] Liu K, Guo L, Li H H, Chen J S. Fusion of infrared and visible light images based on region segmentation. Journal of Aeronautics, 2009, 22(1): 75−80

[6] Zhang X Q, Gao Z S, Zhao Y H. Dynamic infrared and visible image sequence fusion based on DT-CWT using GGD. In: Proceedings of the 2008 International Conference on Computer Secience and Information Technoogy. 2008, 875−878

[7] Bartlett MS, Martin H, Sejnowski T J. Face image analysis by unsupervised learning and redundancy reduction. PhD Dissertation. La Jolla: University of California San Diego and the Salk Institute, 1998

[8] Hyväriene A, Karhunen J, Oja E. Independent Component Analysis. London: John Wiley and Sons, 2001

[9] Kwon O W, Lee T W. Phoneme recognition using ICA-based feature extraction and transformation. Signal Processing, 2004, 84: 1005−1019

[10] Dorffner G, Bischof H, Hornik K. Feature extraction using ICA. Lecture Notes in Computer Science, 2001, 2130: 568−573

[11] Zhang L P, Huang X. Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery. Neurocomputing, 2010, 77(4−6): 727−936

[12] Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13: 411−430

[13] Hyvärinen A, Oja E, Hoyer P, Hurri J. Image feature extraction by sparse coding and independent component analysis. In: Proceedings of the 14th International Conference on Pattern Recognition. 1998, 2: 1268−1273

[14] Kwak N, Choi C H, Ahuja N. Face recogniton using feature extraction based on Independent Component Analysis. Image Processing, 2002, 2: 337−340

[15] Mitianoudis N, Stathaki T. Pixel-based and Region-based image fusion schemes using ICA bases. Information Fusion, 2007, 8(2): 131−142

[16] Mitianoudis N, Stathaki T. Image fusion schemes using ICA bases. London: Communications and Signal Processing Group, 2008

[17] Mitianoudis N, Stathaki T. Optimal contrast correction for ICA-based fusion of multimodal images. IEEE Sensors Journal, 2008, 8(12): 2016−2016

[18] Mitianoudis N, Stathaki T. Adaptive image fusion using ICA bases. In: Proceedings of the 2006 International Conference on Acoustics, Speech and Signal Processing. 2006, 829−832

[19] Cvejic N, Bull D, Canagarajah N. Region-based multimodal image fusion using ICA bases. IEEE Sensors Journal, 2007, 7(5): 743−751

[20] Chen F R, Qin F, Peng G X, Chen S Q. Fusion of remote sensing images using improved ICA mergers based on wavelet decomposition. Procedia Engineering, 2012, 29: 2938−2943

[21] Bell A J, Sejnowski T J. The Independent components of natural scenes are edge filters. Vision Research, 1997, 37: 3327−3338

[22] Yang H H, Moody J. Data visualization and feature selection: new algorithms for nongaussian data. In: Proceedings of Advances in Neural Information Processing Systems. 1999, 687−693

[23] Lewicki S M, Sejnowski T J. Learning overcomplete representations. Neural Computation, 2000, 12(2): 337−365

[24] Hyvärinen A, Oja E. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Research, 2001, 41(18): 2413−2423

[25] Glasner D, Bagon S, Irani M. Super-Resolution from a Single Image. In: Proceedings of the 2009 International Conference on Computer Vision. 2009, 349−356

[26] Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3): 626−634

[27] Zhang Z, Blum R S. A categorization of multiscale-decompositionbasedimage fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE, 1999, 87(8): 1315−1326

[28] Pajares G, Cruz J. A wavelet-based image fusion tutorial. Pattern Recognition, 2004, 37(9): 1855−1872

[29] The image fusion server.

[30] Xydeas C S, Petrovic V. Objective pixel-level image fusion performance measure. Electronics Letters, 2000, 36(4): 308−309

[31] Piella G, Heijmans H. A new quality metric for image fusion. In: Proceedings of the 2003 International Conference on Image Processing. 2003, 3: 173−176

[32] Petrovic V. Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 2007, 8(2): 208−216

[33] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736−3745

[34] Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information Fusion, 2012, 13(1): 10−19

[35] Naidu VPS. Image fusion technique using multi-resolution singular value decomposition. Defence Science Journal, 2011, 61(5): 479−484

[36] Li S T, Yang B, Hu J W. Performance comparison of different multiresolution transforms for image fusion. Information Fusion, 2011, 12: 74−84

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg