A novel image fusion algorithm based on nonsubsampled shearlet transform

Optik

Volumn 125, Issue 10, 2014, Pages 2274-2282

  Yin, Ming ^a   Liu, Wei ^a   Zhao, Xia ^b   Yin, Yanjun ^c   Guo, Yu ^a  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b TONGLING UNIVERSITY   (China)

^c SOUTHEAST UNIVERSITY   (China)

Author keywords

Image fusion; Nonsubsampled shearlet transform; Singular value decomposition; Sum modified Laplacian

Indexed keywords

ALGORITHMS; IMAGE FUSION; SINGULAR VALUE DECOMPOSITION;

IMAGE FUSION ALGORITHMS; IMAGE FUSION METHODS; MULTI-SCALE TRANSFORMS; MULTI-SOURCE IMAGES; OBJECTIVE EVALUATION CRITERIA; SHEARLET TRANSFORMS; SINGULAR VALUE DECOMPOSITION METHOD; SUM-MODIFIED-LAPLACIAN;

IMAGE PROCESSING;

EID: 84899645256     PISSN: 00304026     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijleo.2013.10.064     Document Type: Article

References (27)

1. R.S. Blum, Z. Liu, Multi-sensor Image Fusion and Its Applications, CRC Press, Boca Raton, 2005, pp. 1-10.
2. W. Huang, Z.L. Jing, Evaluation of focus measures in multi-focus image fusion, Pattern Recogn. Lett. 28 (2007) 493-500.
3. S.T. Li, B. Yang, Multi-focus image fusion using region segmentation and spatialfrequency, Image Vision Comput. 26 (2008) 971-979.
4. S.T. Li, J.T. Kwok, Y.N. Wang, Using the discrete wavelet frame transform tomerge Landsat TM and SPOT panchromatic images, Inform. Fusion 3 (2002)17-23.
5. H.H. Wang, A new multiwavelet-based approach to image fusion, J. Math. Imag-ing Vision 21 (2004) 177-192.
6. J.J. Lewis, R.J.O. Callaghan, S.G. Nikolov, et al., Pixel-and region-based imagefusion with complex wavelets, Inform. Fusion 8 (2007) 119-130.
7. W. Li, X.F. Zhu, An image fusion algorithm based on second generationwavelet transform and its performance evaluation, Acta Aotomat. Sin. 33 (2007)817-822.
8. E. Candes, Ridgelets: Theory and Applications, Department of Statistics, Stan-ford University, USA, 1998.
9. J.L. Starck, E.J. Candes, D.L. Donoho, The curvelet transform for image denoising, IEEE Trans. Image Process. 21 (2002) 131-141.
10. M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multires-olution image representation, IEEE Trans. Image Process. 14 (2005) 2091-2106.
11. Q.G. Miao, B.S. Wang, A novel image fusion method using contourlet trans-form, in: Proc. 2006 International Conference on Communications, Circuits andSystems Processing, 2006, pp. 548-552.
12. L. Yang, B.L. Guo, W. Ni, Multimodality medical image fusion based on multi-scale geometric analysis of contourlet transform, Neurocomputing 72 (2008)203-211.
13. A.L. Cunha, J.P. Zhou, M.N. Do, The nonsubsampled contourlet transform: the-ory, design and application, IEEE Trans. Image Process. 15 (2006) 3089-3101.
14. Q. Zhang, B.L. Guo, Multifocus fusion using the nonsubsampled contourlettransform, Signal Process. 89 (2009) 1334-1346.
15. H.F. Li, Y. Chai, Z.F. Li, Multi-focus image fusion based on nonsubsampled con-tourlet transform and focused regions detection, Optik 124 (2013) 40-51.
16. D. Labate, W. Lim, G. Kutyniok, et al., Sparse multidimensional representationusing shearlets, in: Wavelets XI (San Diego, CA, 2005), SPIE Proc., vol. 5914, SPIE, Bellingham, WA, 2005, pp. 254-262.
17. G. Easley, D. Labate, W.Q. Lim., Sparse directional image representations usingthe discrete shearlet transform, Appl. Comput. Harmonic Anal. 25 (2008) 25-46.
18. S. Yi, D. Labate, G.R. Easley, et al., A shearlet approach to edge analysis anddetection, IEEE Trans. Image Process. 18 (2009) 929-941.
19. C. Deng, S. Wang, X. Chen, Remote sensing images fusion algorithm based onshearlet transform, in: Proceeding of International Conference on Environmen-tal Science and Information Application Technology, ACM, Wuhan, China, 2009, pp. 451-454.
20. Q.G. Miao, C. Shi, P.F. Xu, et al., A novel algorithm of image fusion using shearlets, Opt. Commun. 284 (2011) 1540-1547.
21. L. Wang, B. Li, L.F. Tian, Multi-modal medical image fusion using the inter-scaleand intra-scale dependencies between image shift-invariant shearlet coeffi-cients, Inform. Fusion (2012), http://dx.doi.org/10.1016/j.inffus.2012. 03.002.
22. Z. Zhang, R.S. Blum, A categorization of multiscale-decomposition-based imagefusion schemes with a performance study for a digital camera application, Proc.IEEE 87 (1999) 1315-1326.
23. H. Takeda, S. Farsiu, P. Milanfar, Kernel regression for image pro-cessing and reconstruction, IEEE Trans. Image Process. 16 (2007)349-366.
24. T. Thaipanich, B.T. Oh, P.H. Wu, et al., Improved image denoising with adap-tive nonlocal means (ANL-Means) algorithm, IEEE Trans. Consum. Electron. 56(2010) 2623-2630.
25. Y. Chai, H.F. Li, J.F. Qu, Image fusion scheme using a novel dual-channelPCNN in lifting stationary wavelet domain, Opt. Commun. 283 (2010)3591-3602.
26. G.H. Qu, D.L. Zhang, P.F. Yan, Information measure for performance of imagefusion, Electron. Lett. 38 (2002) 313-315.
27. V. Petrovic, C. Xydeas, On the effects of sensor noise in pixel-level image fusionperformance, in: Proc. of the Third International Conference on Image Fusion, vol. 2, IEEE Press, 2000, pp. 14-19.