An Improved Image Registration Method for Infrared and Visible Images

  • Ningning DingEmail author
  • Jingchang Zhuge
  • Shujian Xing
  • Yang Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)


Aiming at solve the problem that it is difficult to match the infrared and visible image of aircraft surface in the same scene, an improved registration algorithm is proposed. It is aiming to obtain more complementary information of residual ice. Firstly, the infrared image enhanced by the image enhancement algorithm based on fuzzy logic, so that the details and the contour become more clear. It also effectively reduces the number of feature points to be extracted. Secondly, in order to solve the problem of high mismatch rate of speeded up robust features (SURF) algorithm, the constraint condition of slope consistency has been used to eliminate the number of mismatch points. Finally, the RANSAC algorithm is used to further improve the matching speed and accuracy. Experimental results show that the proposed method has better rapidity and accuracy.


Image registration Fuzzy logic SURF algorithm Slope consistency 



This work was supported in part by the Open Fund of Tianjin Key Lab for Advanced Signal Processing under Grant 2017 ASP-TJ02, the National Natural Science Foundation of China under Grant 61405246, and CAUC Fund under Grant 3122017005.


  1. 1.
    Zhang, Z., Blum, R.S.: A hybrid image registration technique for camera image fusion application. J. Inf. Fus. 2(2), 135–149 (2001)Google Scholar
  2. 2.
    Li, H., Liu, L., Huang, W., Yue, C.: An improved fusion algorithm for infrared and visible images based on multi-scale transform. J. Infrared Phys. Technol. 74, 28–37 (2016)CrossRefGoogle Scholar
  3. 3.
    Liu, S., Piao, Y., Tahir, M.: Research on fusion technology based on low-light visible image and infrared image. J. Opt. Eng. 55(12), 123104 (2016)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speed up robust features. J. Comput. Vis. Image Underst. 110(3), 346–359 (2008)Google Scholar
  5. 5.
    Xu, J.-x., Lu, Q.-w., Yun-peng, M.A., Qian, R.: Registration method between infrared and visible images of electrical equipment based on slope consistency. J. Optoelectron. Laser 28(07), 794–802 (2017)Google Scholar
  6. 6.
    Rahman, M.A., Liu, S., Wong, C.Y., et al.: Multi-focal image fusion using degree of focus and fuzzy logic. J. Dig. Sig. Process. 1(185), 1–19 (2016)Google Scholar
  7. 7.
    Hu, M. ,Chen, J., Shi, C.: Three-dimensional mapping based on SIFT and RANSAC for mobile robot. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 139–144. Shenyang Institute of Automation, Shenyang 2015Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. J. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of International Conference on Computer Vision, pp. 1150–1157 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ningning Ding
    • 1
    Email author
  • Jingchang Zhuge
    • 1
  • Shujian Xing
    • 1
  • Yang Wang
    • 2
  1. 1.College of Electronic Information and AutomationCivil Aviation University of ChinaTianjinChina
  2. 2.Tianjin Key Lab for Advanced Signal ProcessingCivil Aviation University of ChinaTianjinChina

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