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Evidence-Based Image Registration and Its Effect on Image Fusion

  • Ujwala PatilEmail author
  • Ramesh Ashok Tabib
  • Rohan Raju Dhanakshirur
  • Uma Mudenagudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)

Abstract

In this paper, we propose Evidence-based technique for image registration. In our previous work, we proposed hierarchical model for image registration using Normalized Mutual Information (NMI) as similarity metric. In few cases, we observe atypical behavior of NMI and infer NMI alone is not sufficient to optimize the transformation matrix, to address this problem in this paper we propose evidence-based image registration using Structural Similarity (SSIM) and NMI as evidences. Atypical behavior of NMI is addressed in evidence- based image registration. We also propose evidence-based framework for image fusion and show image fusion is sensitive to the registration of input observations. Multi-temporal image fusion is challenging due to the presence of high mutual information among them. To address this, we formulate an evidence-based fusion framework with weighted combination of observations, considering Confidence Factor (CF) as weights. CFs for fusion are generated using principal components and distance of registered input observations from reference as evidences. Dempster–Shafer Combination Rule (DSCR) is used to combine the evidences to generate CF. We compare the results with state-of-the-art registration techniques.

Keywords

Image registration Image fusion Confidence factor (CF) Dempster–Shafer combination rule (DSCR) Evidence parameter 

References

  1. 1.
    Amintoosi, M., Fathy, M., Mozayani, N.: Precise image registration with structural similarity error measurement applied to super resolution. EURASIP J. Adv. Signal Process. 12, 1–7 (2009)Google Scholar
  2. 2.
    Bardera, A., Feixas, M., Boada, I., Sbert, M.: Compression based image registration. IEEE Int. Symp. Inf. Theory. 6, 436–440 (2006)Google Scholar
  3. 3.
    Bergen, J.R., Anandan, P., Hanna, K.J., Rajesh, H., Zhiyong, Bin Gu, Lin.: Hierarchical model based motion estimation. In: Proceedings of the European Conference on Computer Vision, vol. 2, pp. 164–173 (1992)Google Scholar
  4. 4.
    Bhist, S.S., Gupta, B., Rahi, P.: Image registration concepts and techniques: a review. Int. J. Eng. Res. Appl. (2014)Google Scholar
  5. 5.
    Forsberg, D.: Robust image registration for improved clinical efficiency. Ph.D. thesis, Linkoping University (2013)Google Scholar
  6. 6.
    Gayathri, N., Deepa, P.L.: Multi-focus color image fusion using NSCT and PCNN. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 173–178 (2016)Google Scholar
  7. 7.
    Kalaivani, K., Phamila, Y.A.V.: Analysis of image fusion techniques based on quality assessment techniques. Indian J. Sci. Technol. 1–8 (2016)Google Scholar
  8. 8.
    Lakshmi, K.D., Vaithiyanathan, V.: Image registration techniques based on the scale invariant feature transform. IETE Tech. Rev. 34(1), 22–29 (2017)CrossRefGoogle Scholar
  9. 9.
    Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)CrossRefGoogle Scholar
  10. 10.
    Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 24, 147–164 (2015)CrossRefGoogle Scholar
  11. 11.
    Ma, J., Zhou, H., Zhao, J., Gao, Y., Jiang, J., Tian, J.: Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans. Geosci. Remote Sens. 53(12), 6469–6481 (2015)CrossRefGoogle Scholar
  12. 12.
    Ma, K., Li, H., Yong, H., Wang, Z., Meng, D., Zhang, L.: Robust multi-exposure image fusion: a structural patch decomposition approach. IEEE Trans. Image Process. 26(5), 2519–2532 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mohod, N.P., Ladhake, S.A.: Polar transform in image registration. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 603–606 (2013)Google Scholar
  14. 14.
    Mudenagudi, U., Banerjee, S., Kalra, P.K.: Space-time super-resolution using graph-cut optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 995–1008 (2011)CrossRefGoogle Scholar
  15. 15.
    Naidu, V.P.S., Elias, B.: A novel image fusion technique using DCT based Laplacian pyramid. Int. J. Inven. Eng. Sci. (IJIES) ISSN 2319–9598 (2013)Google Scholar
  16. 16.
    Patil, U., Mudengudi, U.: Image fusion using hierarchical PCA. In: 2011 International Conference on Image Information Processing (ICIIP), pp. 1–6 (2011)Google Scholar
  17. 17.
    Patil, U., Mudengudi, U., Ganesh, K., Patil, R.: Image fusion framework. In: Second International Conference CNC 2011, Bangalore, India, 10–11 March 2011. Proceedings, pp. 653–657. Springer, Berlin (2011)CrossRefGoogle Scholar
  18. 18.
    Patil, U., Patil, R., Kalyani, R., Mudenagudi, U.: Robust registration for image fusion, pp. 1–5Google Scholar
  19. 19.
    Tabib, R.A., Patil, U., Ganihar, S.A., Trivedi, N., Mudenagudi, U.: Decision fusion for robust horizon estimation using Dempster Shafer combination rule. In: 2013 Fourth National Conference on NCVPRIPG, pp. 1–4 (2013)Google Scholar
  20. 20.
    Ward, G.: Fast, robust image registration for compositing high dynamic range photographs from handled exposures. J. Graph. Tools 8, 17–30 (2012)CrossRefGoogle Scholar
  21. 21.
    Wolberg, G., Zokai, S.: Robust image registration using log polar transform. In: IEEE Conference on Image Processing, Canada (2000)Google Scholar
  22. 22.
    Zitova, B., Flusser, J.: Image registration methods: a survey. J. Image Vis. Comput. 21, 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ujwala Patil
    • 1
    Email author
  • Ramesh Ashok Tabib
    • 1
  • Rohan Raju Dhanakshirur
    • 1
  • Uma Mudenagudi
    • 1
  1. 1.School of Electronics and CommunicationKLE Technological UniversityHubballiIndia

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