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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inf. Fusion 45, 153–178 (2018)
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)
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)
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)
Liu, Z., Tsukada, K., Hanasaki, K., Ho, Y., Dai, Y.: Image fusion by using steerable pyramid. Pattern Recogn. Lett. 22(9), 929–939 (2001)
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)
Zhan, L., Zhuang, Y., Huang, L.: Infrared and visible images fusion method based on discrete wavelet transform. J. Comput. 28, 57–71 (2017)
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)
Zou, Y., Liang, X., Wang, T.: Visible and infrared image fusion using the lifting wavelet. TELKOMNIKA Indonesian J. Electr. Eng. 11 (2013)
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)
Chai, P., Luo, X., Zhang, Z.: Image fusion using quaternion wavelet transform and multiple features. IEEE Access 5, 6724–6734 (2017)
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)
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)
Bavirisetti, D.P., Dhuli, R.: Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phys. Technol. 76, 52–64 (2016)
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)
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)
Hu, J., Li, S.: The multiscale directional bilateral filter and its application to multisensor image fusion. Inf. Fusion 13(3), 196–206 (2012)
Toet, A., Hogervorst, M.A.: Multiscale image fusion through guided filtering. In: Target and Background Signatures II (2016)
Yang, B., Jing, Z.-L., Zhao, H.-T.: Review of pixel-level image fusion. J. Shanghai Jiaotong Univ. (Sci.) 15(1), 6–12 (2010)
Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion 4(4), 259–280 (2003)
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)
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)
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)
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)
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)
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)
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)
Shodhganga.inflibnet.ac.in/jspui/bitstream/10603/151753/9/09_chapter%201.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Patel, A., Chaudhary, J. (2020). A Review on Infrared and Visible Image Fusion Techniques. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-28364-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28363-6
Online ISBN: 978-3-030-28364-3
eBook Packages: EngineeringEngineering (R0)