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.
A Mahmood, PM Tudor, W Oxford, R Hansford, J D B Nelson, N G Kingsbury, A Katartzis, M Petrou, N Mitianoudis, T Stathaki, A Achim, D Bull, N Canagarajah, S Nikolov, A Loza, and N Cvejic. Applied multi-dimensional fusion. The Computer Journal, 2007, 50(6): 660−673 https://doi.org/10.1093/comjnl/bxm069
3
A Sinha, H M Chen, D G Danu, T Kirubarajan, M Farooq. Estimation and decision fusion: a survey. Neurocomputing, 2008, 71(13−15): 2650−2656 https://doi.org/10.1016/j.neucom.2007.06.016
K Liu, L Guo, H H Li, J S Chen. Fusion of infrared and visible light images based on region segmentation. Journal of Aeronautics, 2009, 22(1): 75−80 https://doi.org/10.1016/S1000-9361(08)60071-0
6
X Q Zhang, Z S Gao, Y H Zhao. 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
MS Bartlett, H Martin, T J Sejnowski. Face image analysis by unsupervised learning and redundancy reduction. PhD Dissertation. La Jolla: University of California San Diego and the Salk Institute, 1998
O W Kwon, T W Lee. Phoneme recognition using ICA-based feature extraction and transformation. Signal Processing, 2004, 84: 1005−1019 https://doi.org/10.1016/j.sigpro.2004.03.004
10
G Dorffner, H Bischof, K Hornik. Feature extraction using ICA. Lecture Notes in Computer Science, 2001, 2130: 568−573 https://doi.org/10.1007/3-540-44668-0
11
L P Zhang, X Huang. Object-oriented subspace analysis for airborne hyperspectral remote sensing imagery. Neurocomputing, 2010, 77(4−6): 727−936
A Hyvärinen, E Oja, P Hoyer, J Hurri. 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
N Kwak, C H Choi, N Ahuja. Face recogniton using feature extraction based on Independent Component Analysis. Image Processing, 2002, 2: 337−340
15
N Mitianoudis, T Stathaki. Pixel-based and Region-based image fusion schemes using ICA bases. Information Fusion, 2007, 8(2): 131−142 https://doi.org/10.1016/j.inffus.2005.09.001
16
N Mitianoudis, T Stathaki. Image fusion schemes using ICA bases. London: Communications and Signal Processing Group, 2008
17
N Mitianoudis, T Stathaki. Optimal contrast correction for ICA-based fusion of multimodal images. IEEE Sensors Journal, 2008, 8(12): 2016−2016 https://doi.org/10.1109/JSEN.2008.2007678
18
N Mitianoudis, T Stathaki. Adaptive image fusion using ICA bases. In: Proceedings of the 2006 International Conference on Acoustics, Speech and Signal Processing. 2006, 829−832 https://doi.org/10.1109/ICASSP.2006.1660471
19
N Cvejic, D Bull, N Canagarajah. Region-based multimodal image fusion using ICA bases. IEEE Sensors Journal, 2007, 7(5): 743−751 https://doi.org/10.1109/JSEN.2007.894926
20
F R Chen, F Qin, G X Peng, S Q Chen. Fusion of remote sensing images using improved ICA mergers based on wavelet decomposition. Procedia Engineering, 2012, 29: 2938−2943 https://doi.org/10.1016/j.proeng.2012.01.418
H H Yang, J Moody. Data visualization and feature selection: new algorithms for nongaussian data. In: Proceedings of Advances in Neural Information Processing Systems. 1999, 687−693
A Hyvärinen, E Oja. 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 https://doi.org/10.1016/S0042-6989(01)00114-6
25
D Glasner, S Bagon, M Irani. Super-Resolution from a Single Image. In: Proceedings of the 2009 International Conference on Computer Vision. 2009, 349−356 https://doi.org/10.1109/ICCV.2009.5459271
26
A Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 1999, 10(3): 626−634 https://doi.org/10.1109/72.761722
27
Z Zhang, R S Blum. 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 https://doi.org/10.1109/5.775414
C S Xydeas, V Petrovic. Objective pixel-level image fusion performance measure. Electronics Letters, 2000, 36(4): 308−309 https://doi.org/10.1049/el:20000267
31
G Piella, H Heijmans. A new quality metric for image fusion. In: Proceedings of the 2003 International Conference on Image Processing. 2003, 3: 173−176
M Elad, M Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736−3745 https://doi.org/10.1109/TIP.2006.881969
VPS Naidu. Image fusion technique using multi-resolution singular value decomposition. Defence Science Journal, 2011, 61(5): 479−484
36
S T Li, B Yang, J W Hu. Performance comparison of different multiresolution transforms for image fusion. Information Fusion, 2011, 12: 74−84 https://doi.org/10.1016/j.inffus.2010.03.002