This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.
References
-
-
1)
-
[13]. Sulochana, S., Vidhya, R., Manonmani, R.: ‘Optical image fusion using support value transform (SVT) and curvelets’, Optik – Int. J. Light Electron Optics, 2015, 126, (18), pp. 1672–1675 (doi: 10.1016/j.ijleo.2015.04.057).
-
2)
-
[39]. Tsai, W.-T., Qi, G.: ‘Integrated fault detection and test algebra for combinatorial testing in TAAS (testing-as-a-service)’, Simul. Modelling Pract. Theory, 2016, 68, pp. 108–124 (doi: 10.1016/j.simpat.2016.08.003).
-
3)
-
16. Qu, G., Zhang, D., Yan, P.: ‘Information measure for performance of image fusion’, Electron. Lett., 2002, 38, (7), pp. 313–315 (doi: 10.1049/el:20020212).
-
4)
-
[18]. Nejati, M., Samavi, S., Shirani, S.: ‘Multi-focus image fusion using dictionary-based sparse representation’, Inf. Fusion, 2015, 25, pp. 72–84 (doi: 10.1016/j.inffus.2014.10.004).
-
5)
-
[31]. Zhu, Z., Qi, G., Chai, Y., et al: ‘A novel multi-focus image fusion method based on stochastic coordinate coding and local density peaks clustering’, Future Internet, 2016, 8, (4), p. 53 (doi: 10.3390/fi8040053).
-
6)
-
[38]. Deshmukh, M., Bhosale, U.: ‘Image fusion and image quality assessment of fused images’, Int. J. Image Process. (IJIP), 2010, 4, (5), pp. 484–508.
-
7)
-
[29]. Wang, K., Qi, G., Zhu, Z., et al: ‘A novel geometric dictionary construction approach for sparse representation based image fusion’, Entropy, 2017, 19, (7), p. 306 (doi: 10.3390/e19070306).
-
8)
-
6. Xydeas, C., Petrović, V.: ‘Objective image fusion performance measure’, Electron. Lett., 2000, 36, (4), pp. 308–309 (doi: 10.1049/el:20000267).
-
9)
-
[4]. Tsai, W.T., Qi, G., Chen, Y.: ‘A cost-effective intelligent configuration model in cloud computing’. 32nd Int. Conf. on Distributed Computing Systems Workshops, Macau, China, 2012, pp. 400–408.
-
10)
-
[19]. Zhu, Z., Qi, G., Chai, Y., et al: ‘A novel visible-infrared image fusion framework for smart city’, Int. J. Simul. Process Model., 2018, 13, (2), pp. 144–155 (doi: 10.1504/IJSPM.2018.091691).
-
11)
-
[15]. Seal, A., Bhattacharjee, D., Nasipuri, M.: ‘Human face recognition using random forest based fusion of à-trous wavelet transform coefficients from thermal and visible images’, AEU – Int. J. Electron. Commun., 2016, 70, (8), pp. 1041–1049 (doi: 10.1016/j.aeue.2016.04.016).
-
12)
-
[3]. Tsai, W.T., Qi, G.: ‘DICB: dynamic intelligent customizable benign pricing strategy for cloud computing’. IEEE Fifth Int. Conf. on Cloud Computing, Honolulu, HI, USA, 2012, pp. 654–661.
-
13)
-
22. Aharon, M., Elad, M., Bruckstein, A.: ‘K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation’, IEEE Trans. Signal Process., 2006, 54, pp. 4311–4322 (doi: 10.1109/TSP.2006.881199).
-
14)
-
[33]. Ratnarajah, T., Vaillancourt, R., Alvo, M.: ‘Eigenvalues and condition numbers of complex random matrices’, SIAM J. Matrix Anal. Appl., 2004, 26, (2), pp. 441–456 (doi: 10.1137/S089547980342204X).
-
15)
-
[16]. QU, X.-B., Yan, J.-W., Xiao, H.-Z., et al: ‘Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain’, Acta Autom. Sin., 2008, 34, (12), pp. 1508–1514 (doi: 10.3724/SP.J.1004.2008.01508).
-
16)
-
[23]. Ibrahim, R., Alirezaie, J., Babyn, P.: ‘Pixel level jointed sparse representation with RPCA image fusion algorithm’. 38th Int. Conf. on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, 2015, pp. 592–595.
-
17)
-
[43]. Zuo, Q., Xie, M., Qi, G., et al: ‘Tenant-based access control model for multi-tenancy and sub-tenancy architecture in software-as-a-service’, Front. Comput. Sci., 2017, 11, (3), pp. 465–484 (doi: 10.1007/s11704-016-5081-x).
-
18)
-
[11]. Luo, X., Zhang, Z., Wu, X.: ‘A novel algorithm of remote sensing image fusion based on shift-invariant shearlet transform and regional selection’, AEU – Int. J. Electron. Commun., 2016, 70, (2), pp. 186–197 (doi: 10.1016/j.aeue.2015.11.004).
-
19)
-
[20]. Kim, M., Han, D.K., Ko, H.: ‘Joint patch clustering-based dictionary learning for multimodal image fusion’, Inf. Fusion, 2016, 27, pp. 198–214 (doi: 10.1016/j.inffus.2015.03.003).
-
20)
-
[2]. Qi, G., Tsai, W.-T., Li, W., et al: ‘A cloud-based triage log analysis and recovery framework’, Simul. Modelling Pract. Theory, 2017, 77, pp. 292–316 (doi: 10.1016/j.simpat.2017.07.003).
-
21)
-
[5]. Li, H., Qiu, H., Yu, Z., et al: ‘Multifocus image fusion via fixed window technique of multiscale images and non-local means filtering’, Signal Process., 2017, 138, pp. 71–85 (doi: 10.1016/j.sigpro.2017.03.008).
-
22)
-
[6]. Qi, G., Wang, J., Zhang, Q., et al: ‘An integrated dictionary-learning entropy-based medical image fusion framework’, Future Internet, 2017, 9, (4), p. 61 (doi: 10.3390/fi9040061).
-
23)
-
26. Sheikh, H.R., Bovik, A.C.: ‘Image information and visual quality image processing’, IEEE Trans. Image Process., 2006, 15, pp. 430–444 (doi: 10.1109/TIP.2005.859378).
-
24)
-
[7]. Li, H., Liu, X., Yu, Z., et al: ‘Performance improvement scheme of multifocus image fusion derived by difference images’, Signal Process., 2016, 128, pp. 474–493 (doi: 10.1016/j.sigpro.2016.05.015).
-
25)
-
[32]. Takeda, H., Farsiu, S., Milanfar, P.: ‘Kernel regression for image processing and reconstruction’, IEEE Trans. Image Process., 2007, 16, (2), pp. 349–366 (doi: 10.1109/TIP.2006.888330).
-
26)
-
[24]. Qi, G., Zhu, Z., Erqinhu, K., et al: ‘Fault-diagnosis for reciprocating compressors using big data and machine learning’, Simul. Modelling Pract. Theory, 2018, 80, pp. 104–127 (doi: 10.1016/j.simpat.2017.10.005).
-
27)
-
[41]. Tsai, W., Qi, G., Zhu, Z.: ‘Scalable SAAS indexing algorithms with automated redundancy and recovery management’, Int. J. Softw. Inf., 2013, 7, (1), pp. 63–84.
-
28)
-
[8]. Li, H., Yu, Z., Mao, C.: ‘Fractional differential and variational method for image fusion and super-resolution’, Neurocomputing, 2016, 171, pp. 138–148 (doi: 10.1016/j.neucom.2015.06.035).
-
29)
-
[14]. Yu, B., Jia, B., Ding, L., et al: ‘Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion’, Neurocomputing, 2016, 182, pp. 1–9 (doi: 10.1016/j.neucom.2015.10.084).
-
30)
-
[34]. Bigün, J., Granlund, G.H., Wiklund, J.: ‘Multidimensional orientation estimation with applications to texture analysis and optical flow’, IEEE Trans. Pattern Anal. Mach. Intell., 1991, 13, (8), pp. 775–790 (doi: 10.1109/34.85668).
-
31)
-
[27]. Yang, S., Wang, M., Chen, Y., et al: ‘Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding’, IEEE Trans. Image Process., 2012, 21, (9), pp. 4016–4028 (doi: 10.1109/TIP.2012.2201491).
-
32)
-
[35]. Chatterjee, P., Milanfar, P.: ‘Clustering-based denoising with locally learned dictionaries’, IEEE Trans. Image Process., 2009, 18, (7), pp. 1438–1451 (doi: 10.1109/TIP.2009.2018575).
-
33)
-
[22]. Li, S., Yin, H., Fang, L.: ‘Group-sparse representation with dictionary learning for medical image denoising and fusion’, IEEE Trans. Biomed. Eng., 2012, 59, (12), pp. 3450–3459 (doi: 10.1109/TBME.2012.2217493).
-
34)
-
[36]. Yang, B., Li, S.: ‘Pixel-level image fusion with simultaneous orthogonal matching pursuit’, Inf. Fusion, 2012, 13, (1), pp. 10–19 (doi: 10.1016/j.inffus.2010.04.001).
-
35)
-
[45]. Han, Y., Cai, Y., Cao, Y., et al: ‘A new image fusion performance metric based on visual information fidelity’, Inf. Fusion, 2013, 14, (2), pp. 127–135 (doi: 10.1016/j.inffus.2011.08.002).
-
36)
-
[9]. Pajares, G., de la Cruz, J.M.: ‘A wavelet-based image fusion tutorial’, Pattern Recognit., 2004, 37, (9), pp. 1855–1872 (doi: 10.1016/j.patcog.2004.03.010).
-
37)
-
7. Makbol, N.M., Khoo, B.E.: ‘Robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition’, AEU-Int. J. Electron. C, 2013, 67, (2), pp. 102–112 (doi: 10.1016/j.aeue.2012.06.008).
-
38)
-
[25]. Zhu, Z., Sun, J., Qi, G., et al: ‘Frequency regulation of power systems with self-triggered control under the consideration of communication costs’, Appl. Sci., 2017, 7, (7), p. 688 (doi: 10.3390/app7070688).
-
39)
-
[1]. Wu, W., Tsai, W.T., Jin, C., et al: ‘Test-algebra execution in a cloud environment’. IEEE 8th Int. Symp. on Service Oriented System Engineering, Oxford, UK, 2014, pp. 59–69.
-
40)
-
[12]. Liu, X., Zhou, Y., Wang, J.: ‘Image fusion based on shearlet transform and regional features’, AEU – Int. J. Electron. Commun., 2014, 68, (6), pp. 471–477 (doi: 10.1016/j.aeue.2013.12.003).
-
41)
-
[21]. Guo, M., Zhang, H., Li, J., et al: ‘An online coupled dictionary learning approach for remote sensing image fusion’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (4), pp. 1284–1294 (doi: 10.1109/JSTARS.2014.2310781).
-
42)
-
[28]. Zhu, Z., Qi, G., Chai, Y., et al: ‘A geometric dictionary learning based approach for fluorescence spectroscopy image fusion’, Appl. Sci., 2017, 7, (2), p. 161 (doi: 10.3390/app7020161).
-
43)
-
[37]. Yin, H., Li, S., Fang, L.: ‘Simultaneous image fusion and super-resolution using sparse representation’, Inf. Fusion, 2013, 14, (3), pp. 229–240 (doi: 10.1016/j.inffus.2012.01.008).
-
44)
-
[17]. Yin, H., Li, Y., Chai, Y., et al: ‘A novel sparse-representation-based multi-focus image fusion approach’, Neurocomputing, 2016, 216, pp. 216–229 (doi: 10.1016/j.neucom.2016.07.039).
-
45)
-
[30]. Zhu, Z., Yin, H., Chai, Y., et al: ‘A novel multi-modality image fusion method based on image decomposition and sparse representation’, Inf. Sci., 2018, 432, pp. 516–529 (doi: 10.1016/j.ins.2017.09.010).
-
46)
-
[46]. Zhang, Q., Levine, M.D.: ‘Robust multi-focus image fusion using multi-task sparse representation and spatial context’, IEEE Trans. Image Process., 2016, 25, (5), pp. 2045–2058 (doi: 10.1109/TIP.2016.2524212).
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