Abstract
Combined the advantages of time-frequency separation of complex shearlet (CST) with the feature of guided filtering, a new image fusion algorithm based on CST domain and guided filtering is proposed. Firstly, CST is utilized for decomposition of the source images. Secondly, two scale guided filtering fusion rule is applied to the low frequency coefficients. Thirdly, larger sum-modified-Laplacian with guided filtering fusion rule is applied to the high frequency coefficients. Finally, the fused image is gained by the inverse CST. The algorithm can not only preserve the information of the source images well, but also improve the spatial continuity of fusion image. Experimental results show that the proposed method is superior to other current popular ones both in subjective visual and objective performance.
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Cunha, A. L., Zhou, J. P., & Do, M. N. (2006). The nonsubsampled contourlet transform:Theory, design and application. IEEE Transactions on Image Processing, 15(10), 3089–3101.
Do, M. N., & Vetterli, M. (2005). The contourlet transform: An efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091–2106.
Draper, N., & Smith, H. (1981). Applied regression analysis. New York: Wiley.
Easley, G., Labate, D., & Lim, W. Q. (2008). Sparse directional image representation using the discrete shearlets transform. Applied and Computational Harmonic Analysis, 25(1), 25–46.
Eslami, R., & Radha, H. (2004). Wavelet based contourlet transform and it ’s application to image coding. In IEEE international conference on image processing, Singapore (pp. 3189–3192).
Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 67:1–67:10.
Geng, P., Wang, Z., Zhang, Z., et al. (2012). Image fusion by pulse couple neural network with shearlet. Optical Engineering, 51(6), 067005-1–067005-7.
He, K. M., Sun, J., & Tang, X. O. (2013). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409.
Jia, Y. H. (1998). Fusion of landsat TM and SAR images based on principal component analysis. Remote Sensing Technology and Application, 13(1), 46–49.
Kingsbury, N. (1999). Image processing with complex wavelets. Philosophical Transactions: Mathematical Physical and Engineering Sciences, 357(1760), 2543–2560.
Kutyniok, G., Lemvig, J., & Lim, W. Q. (2011). Compactly supported shearlets are optimally sparse. Journal of Approximation Theory, 163(11), 1564–1589.
Li, S. T., Kang, X. D., & Hu, J. W. (2013). Image fusion with guided filtering. IEEE Transactions on Image Processing, 22(7), 2864–2875.
Lim, W. Q. (2010). The discrete shearlets transform: A new directional transform and compactly supported shearlets frames. IEEE Transactions on Image Processing, 19(5), 1166–1180.
Liu, K., Guo, L., & Chen, J. S. (2011). Contourlet transform for image fusion using cycle spinning. Journal of Systems Engineering and Electronics, 22(2), 353–357.
Liu, S. Q., Hu, S. H., & Xiao, Y. (2013). SAR image de-noising based on complex shearlet transform domain gaussian mixture model. Acta Aeronautica et Astronautica Sinica, 34(1), 173–180. (in Chinese).
Liu, S. Q., Hu, S. H., & Xiao, Y. (2014). Image separation using wavelet-complex shearlet dictionary. Journal of Systems Engineering and Electronics, 25(2), 314–321.
Liu, S. Q., Hu, S. H., Xiao, Y., et al. (2014). Bayesian Shearlet shrinkage for SAR image de-noising via sparse representation. Multidimensional Systems and Signal Processing, 25(4), 683–701.
Liu, S., Zhao, J., & Shi, M. Z. (2015). Medical image fusion based on rolling guidance filter and spiking cortical model. Computational and Mathematical Methods in Medicine, 2015, 1–9.
Miao, Q. G., Shi, C., & Xu, P. F. (2011). A novel algorithm of image fusion using shearlets. Optics Communications, 284(6), 1540–1547.
Miao, Q. G., Shi, C., & Xu, P. F. (2011). Multi-focus image fusion algorithm based on shearlets. Chinese Optics Letters, 9(4), 041001.1–041001.5.
Pajares, G., & Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855–1872.
Qu, X. B., Yan, J. W., Xiao, H. Z., et al. (2008). Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica, 34(12), 1508–1514.
Qu, X. B., Yan, J. W., & Yang, G. D. (2009). Sum-modified-Laplacian-based multi-focus image fusion method in sharp frequency localized contourlet transform domain. Optics and Processing Engineering, 17(5), 1203–1212.
Zhang, Q., & Guo, B. (2009). Multifocus image fusion using the nonsubsanpled contourlet transforms. Signal Processing, 89(7), 1334–1346.
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This work was supported in part by Natural Science Foundation of China under Grant No. 61401308, Natural Science Foundation of Hebei Province under Grant No. 2013210094, Natural Science Foundation of Hebei University under Grant No. 2014-303, Science and technology support project of Baoding City under Grant No. 15ZG016, Open laboratory project of Hebei University under Grant No. sy2015009 and sy2015057.
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Liu, S., Shi, M., Zhu, Z. et al. Image fusion based on complex-shearlet domain with guided filtering. Multidim Syst Sign Process 28, 207–224 (2017). https://doi.org/10.1007/s11045-015-0343-6
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DOI: https://doi.org/10.1007/s11045-015-0343-6