Advertisement

A Review on Infrared and Visible Image Fusion Techniques

  • Ami Patel
  • Jayesh ChaudharyEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)

Abstract

The term fusion means in moderate approach to extract the information acquired in several domains. The term infrared and visible image fusion has been intended to find compatible fused image with detailed textures of visible images and an impressive infrared object area. We therefore combine infrared and visible images to create solitary image. Current real - time applications that encourage image fusion including military surveillance, automate agricultural, object recognition, remote sensing, and medical applications. The concept of merging two or more than two images using the various image fusion schemes. This paper begins with the background information on the image fusion. Secondly, infrared and visible image fusion rest on multi-scale transformation of existing techniques are reviewed with all the merits and demerits of the same table lists. Further section elaborates fusion strategies and fusion performance evaluation metrics are summarized.

Keywords

Infrared image Visible image Image fusion Infrared and visible image fusion 

References

  1. 1.
    Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inf. Fusion 45, 153–178 (2018)CrossRefGoogle Scholar
  2. 2.
    Jin, X., Jiang, Q., Yao, S., Zhou, D., Nie, R., Hai, J., He, K.: A survey of infrared and visual image fusion methods. Infrared Phys. Technol. 85, 478–501 (2017)CrossRefGoogle Scholar
  3. 3.
    Dogra, A., Goyal, B., Agrawal, S.: From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE Access 5, 16040–16067 (2017)CrossRefGoogle Scholar
  4. 4.
    Yu, X., Ren, J., Chen, Q., Sui, X.: A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space. Optik – Int. J. Light Electron Opt. 125(20), 6010–6016 (2014)CrossRefGoogle Scholar
  5. 5.
    Liu, Z., Tsukada, K., Hanasaki, K., Ho, Y., Dai, Y.: Image fusion by using steerable pyramid. Pattern Recogn. Lett. 22(9), 929–939 (2001)CrossRefGoogle Scholar
  6. 6.
    Xu, H., Wang, Y., Wu, Y., Qian, Y.: Infrared and multi-type images fusion algorithm based on contrast pyramid transform. Infrared Phys. Technol. 78, 133–146 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhan, L., Zhuang, Y., Huang, L.: Infrared and visible images fusion method based on discrete wavelet transform. J. Comput. 28, 57–71 (2017)Google Scholar
  8. 8.
    Madheswari, K., Venkateswaran, N.: Swarm intelligence based optimisation in thermal image fusion using dual tree discrete wavelet transform. Quant. InfraRed Thermography J. 14(1), 24–43 (2016)CrossRefGoogle Scholar
  9. 9.
    Zou, Y., Liang, X., Wang, T.: Visible and infrared image fusion using the lifting wavelet. TELKOMNIKA Indonesian J. Electr. Eng. 11 (2013) Google Scholar
  10. 10.
    Yan, X., Qin, H., Li, J., Zhou, H., Zong, J.-G.: Infrared and visible image fusion with spectral graph wavelet transform. J. Opt. Soc. Am. A 32, 1643 (2015)CrossRefGoogle Scholar
  11. 11.
    Chai, P., Luo, X., Zhang, Z.: Image fusion using quaternion wavelet transform and multiple features. IEEE Access 5, 6724–6734 (2017)CrossRefGoogle Scholar
  12. 12.
    Quan, S., Qian, W., Guo, J., Zhao, H.: Visible and infrared image fusion based on curvelet transform. In: International Conference on Systems and Informatics (ICSAI) (2014)Google Scholar
  13. 13.
    Li, H., Liu, L., Huang, W., Yue, C.: An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Phys. Technol. 74, 28–37 (2016)CrossRefGoogle Scholar
  14. 14.
    Bavirisetti, D.P., Dhuli, R.: Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phys. Technol. 76, 52–64 (2016)CrossRefGoogle Scholar
  15. 15.
    Yan, X., Qin, H., Li, J., Zhou, H., Zong, J.-G., Zeng, Q.: Infrared and visible image fusion using multiscale directional nonlocal means filter. Appl. Opt. 54(13), 4299 (2015)CrossRefGoogle Scholar
  16. 16.
    Bavirisetti, D.P., Dhuli, R.: Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sens. J. 16(1), 203–209 (2016)CrossRefGoogle Scholar
  17. 17.
    Hu, J., Li, S.: The multiscale directional bilateral filter and its application to multisensor image fusion. Inf. Fusion 13(3), 196–206 (2012)CrossRefGoogle Scholar
  18. 18.
    Toet, A., Hogervorst, M.A.: Multiscale image fusion through guided filtering. In: Target and Background Signatures II (2016)Google Scholar
  19. 19.
    Yang, B., Jing, Z.-L., Zhao, H.-T.: Review of pixel-level image fusion. J. Shanghai Jiaotong Univ. (Sci.) 15(1), 6–12 (2010)CrossRefGoogle Scholar
  20. 20.
    Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion 4(4), 259–280 (2003)CrossRefGoogle Scholar
  21. 21.
    Kalaivani, K., Phamila, Y.A.V.: Analysis of image fusion techniques based on quality assessment metrics. Indian J. Sci. Technol. 9(31), 1–8 (2016)CrossRefGoogle Scholar
  22. 22.
    Meng, F., Song, M., Guo, B., Shi, R., Shan, D.: Image fusion based on object region detection and non-subsampled contourlet transform. Comput. Electr. Eng. 62, 375–383 (2017)CrossRefGoogle Scholar
  23. 23.
    Wu, W., Qiu, Z., Zhao, M., Huang, Q., Lei, Y.: Visible and infrared image fusion using NSST and deep Boltzmann machine. Optik 157, 334–342 (2018)CrossRefGoogle Scholar
  24. 24.
    Paramanandham, N., Rajendiran, K.: Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications. Infrared Phys. Technol. 88, 13–22 (2018)CrossRefGoogle Scholar
  25. 25.
    Bai, X., Zhou, F., Xue, B.: Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform. Opt. Express 19(9), 8444 (2011)CrossRefGoogle Scholar
  26. 26.
    Song, Y., Xiao, J., Yang, J., Chai, Z., Wu, Y.: Research on MR-SVD based visual and infrared image fusion. In: Infrared Technology and Applications, and Robot Sensing and Advanced Control (2016)Google Scholar
  27. 27.
    Falk, H.H.: Prolog to a categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proc. IEEE 87(8), 1313–1314 (1999)CrossRefGoogle Scholar
  28. 28.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Engineering DepartmentSarvajanik College of Engineering and TechnologySuratIndia

Personalised recommendations