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2D-VMD Embedded Fusion of Infrared Polarization and Intensity Images Using Muitiple-Algorithms Based on Their Complementary Relation

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Abstract

The keys to multiple-algorithm fusion methods are the selection of the fusion algorithms and sequence of combination. In this paper, a new multiple-algorithm embedded fusion of infrared polarization and intensity images based on the complementary relation of the algorithms is proposed. First, indexes based on the feature similarities are applied to analyze the complementary relation. Then, in light of the complementary relation, fusion algorithms are selected and the embedded sequence is determined, and a fusion algorithm based on the energy difference degree is used to obtain the low-frequency feature fusion image, and the high-frequency feature fusion images are obtained based on the different combination of the guider filter and non-subsampled shearlet transform (NSST). Finally, the different feature fusion images are combined through two-dimensional variational mode decomposition (2D-VMD). The experiments demonstrate that the proposed method can clearly improve the fusion performance of multiple embedded infrared polarization and intensity images and generate a better image fusion.

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REFERENCES

  1. Wang, X., Liang, J.A., Long, H., Yao, J., Xia, R., He, S., and Jin, W., Experimental study on long wave infrared polarization imaging of typical background and objectives, Infrared Laser Eng., 2016, vol. 45, no. 7, p. 704002.  https://doi.org/10.3788/irla201645.0704002

    Article  Google Scholar 

  2. Zhou, Q. and Zhao, J.-F., Feng, H.-J., Xu, Z.-H., Li, Q., and Chen, Y.-T., Infrared polarization image fusion with non-sampling Shearlets, J. Zhejiang Univ. (Eng. Sci.), 2014, vol. 48, no. 8, pp. 1508–1516.

    Google Scholar 

  3. Du, J., Li, W., Lu, K., and Xiao, B., An overview of multi-modal medical image fusion, Neurocomputing, 2016, vol. 215, pp. 3–20. https://doi.org/10.1016/j.neucom.2015.07.160

    Article  Google Scholar 

  4. Ghassemian, H., A review of remote sensing image fusion methods, Inf. Fusion, 2016, vol. 32, part A, pp. 75–89. https://doi.org/10.1016/j.inffus.2016.03.003

  5. Bharati, S., Khan, T.Z., Podder, P., and Hung, N.Q., A comparative analysis of image denoising problem: Noise models, denoising filters and applications, Cognitive Internet of Medical Things for Smart Healthcare, Hassanien, A.E., Khamparia, A., Gupta, D., Shankar, K., and Slowik, A., Eds., Studies in Systems, Decision and Control, vol. 311, Cham: Springer, 2020, pp. 49–66. https://doi.org/10.1007/978-3-030-55833-8_3

  6. Bharati, S., Podder, P., and Mondal, M.R.H., Hybrid deep learning for detecting lung diseases from X-ray images, Inf. Med. Unlocked, 2020, vol. 20, p. 100391. https://doi.org/10.1016/j.imu.2020.100391

    Article  Google Scholar 

  7. Khamparia, A., Bharat, S., Podder, P., Gupta, D., Khanna, A., Phung, T.K., and Thanh, D.N.H., Diagnosis of breast cancer based on modern mammography using hybrid transfer learning, Multidimensional Syst. Signal Process., 2021, vol. 32, pp. 747–765. https://doi.org/10.1007/s11045-020-00756-7

    Article  MATH  Google Scholar 

  8. Bharati, S., Rahman, M.A., Mandal, S., and Podder, P., Analysis of DWT, DCT, BFO & PBFO algorithm for the purpose of medical image watermarking, Int. Conf. on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 2018, IEEE, 2018, pp. 1–6. https://doi.org/10.1109/CIET.2018.8660796

  9. Li, S. and Yang, B., Multifocus image fusion by combining curvelet and wavelet transform, Pattern Recognit. Lett., 2008, vol. 29, no. 9, pp. 1295–1301. https://doi.org/10.1016/j.patrec.2008.02.002

    Article  Google Scholar 

  10. Kandasamy, K., Manikandan, V., Rajaram, M., and Tamilselvan, K.S., Multimodal medical image fusion using Dual Tree-CWT and Non-Subsampled Contourlet Transform, J. Chem. Pharmaceutical Sci., 2015, no. 9, pp. 9–14.

  11. Bharati, S., Podder, P., and Al-Masud, M.R., Brain magnetic resonance imaging compression using daubechies & biorthogonal wavelet with the fusion of STW and SPIHT, Int. Conf. on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 2018, IEEE, 2018, pp. 1–4.  https://doi.org/10.1109/ICAEEE.2018.8643004

  12. Vijayarajan, R. and Muttan, S., Discrete wavelet transform based principal component averaging fusion for medical images, AEU Int. J. Electron. Commun., 2015, vol. 69, no. 5, pp. 896–902.  https://doi.org/10.1016/j.aeue.2015.02.007

    Article  Google Scholar 

  13. Miao, Q. and Wang, B., A novel image fusion method using WBCT and PCA, Chin. Opt. Lett., 2008, vol. 6, no. 2, pp. 104–107.

    Article  Google Scholar 

  14. Lin, S.-Z., Wang, D.-J., and Zhu, X.-H., and Zhang, S.-M. Fusion of infrared intensity and polarization images using embedded multi-scale transform, Optik, 2015, vol. 126, no. 24, pp. 5127–5133.  https://doi.org/10.1016/j.ijleo.2015.09.154

    Article  Google Scholar 

  15. Huang, W. and Jing, Z., Evaluation of focus measures in multi-focus image fusion, Pattern Recognit. Lett., 2007, vol. 28, no. 4, pp. 493–500.  https://doi.org/10.1016/j.patrec.2006.09.005

    Article  Google Scholar 

  16. Dragomiretskiy, K. and Zosso, D., Two-dimensional variational mode decomposition, Energy Minimization Methods in Computer Vision and Pattern Recognition, Tai, X.C., Bae, E., Chan, T.F., and Lysaker, M., Eds., Lecture Notes in Computer Science, vol. 8932, Cham: Springer, 2015, pp. 197–208. https://doi.org/10.1007/978-3-319-14612-6_15

    Book  MATH  Google Scholar 

  17. Zosso, D., Dragomiretskiy, K., Bertozzi, A., Weiss, P.S., Two-dimensional compact variational mode decomposition, J. Math. Imaging Vision, 2017, vol. 58, pp. 294–320.  https://doi.org/10.1007/s10851-017-0710-z

    Article  MathSciNet  MATH  Google Scholar 

  18. Dragomiretskiy, K. and Zosso, D., Variational mode decomposition, IEEE Trans. Signal Process., 2014, vol. 62, no. 3, pp. 531–544.  https://doi.org/10.1109/TSP.2013.2288675

    Article  MathSciNet  MATH  Google Scholar 

  19. Qu X.-B., Yan J.-W., Xiao H.-Z., and Zhu, Z.-Q., Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain, Acta Autom. Sinica, 2008, vol. 34, no. 12, pp. 1508–1514.  https://doi.org/10.1016/S1874-1029(08)60174-3

    Article  MATH  Google Scholar 

  20. Liu, Y., Liu, S., and Wang, Z., A general framework for image fusion based on multi-scale transform and sparse representation, Inf. Fusion, 2015, vol. 24, pp. 147–164.  https://doi.org/10.1016/j.inffus.2014.09.004

    Article  Google Scholar 

  21. Aslantas, V. and Bendes, E., A new image quality metric for image fusion: The sum of the correlations of differences, AEU Int. J. Electron. Commun., 2015, vol. 69, no. 12, pp. 1890–1896.  https://doi.org/10.1016/j.aeue.2015.09.004

    Article  Google Scholar 

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Funding

The authors are grateful for the financial support provided by the National Natural Science Foundation of China (grant no. 61672472), Ph.D. Project of Nanyang normal University (2019). This work was supported in part by Foundation of ExcellentYoung-Backbone Teacher of Colleges and Universities in Henan Province (grant no. 2019GGJS182) and in part by Key Scientific Research Project of Henan Colleges and Universities (grant no. 21B120001).

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Lei Zhang, Fengbao Yang 2D-VMD Embedded Fusion of Infrared Polarization and Intensity Images Using Muitiple-Algorithms Based on Their Complementary Relation. Aut. Control Comp. Sci. 56, 272–282 (2022). https://doi.org/10.3103/S0146411622030099

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