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Novel infrared and visible image fusion method based on independent component analysis

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Abstract

The goal of infrared (IR) and visible image fusion is for the fused image to contain IR object features from the IR image and retain the visual details provided by the visible image. The disadvantage of traditional fusion method based on independent component analysis (ICA) is that the primary feature information that describes the IR objects and the secondary feature information in the IR image are fused into the fused image. Secondary feature information can depress the visual effect of the fused image. A novel ICA-based IR and visible image fusion scheme is proposed in this paper. ICA is employed to extract features from the infrared image, and then the primary and secondary features are distinguished by the kurtosis information of the ICA base coefficients. The secondary features of the IR image are discarded during fusion. The fused image is obtained by fusing primary features into the visible image. Experimental results show that the proposed method can provide better perception effect.

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Authors and Affiliations

  1. National Key Laboratory of CNS/ATM, School of Electronics and Information Engineering, Beihang University, Beijing, 100191, China

    Yin Lu, Fuxiang Wang, Xiaoyan Luo & Feng Liu

Authors
  1. Yin Lu
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  2. Fuxiang Wang
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  3. Xiaoyan Luo
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  4. Feng Liu
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Corresponding author

Correspondence to Yin Lu.

Additional information

Yin Lu received her BS in electronic and optical engineering from Nanjing University of Science and Technology, China in 2010. Now she is studying the MS degree in School of Electronics and Information Engineering of Beihang University, China. Her current research interests lie in the areas of image fusion and aviation surveillance.

Fuxiang Wang received the BS in 1999 and the PhD in 2007, all from Beihang University, China. He is currently a lectuer with the School of Electronics and Information Engineering, Beihang University, China. His current research interests lie in the areas of blind source separation and their applications.

Xiaoyan Luo received the BS in communication engineering from Taiyuan University of Technology, China in 2004 and received the MS in communication and information system in 2007 and PhD degree in electronics and information engineering in 2011 both from Beihang University, China. She is currently a lectuer with the Image Processing Center, School of Astronautics, Beihang University, China. Her research interests include image fusion and pattern recognition.

Feng Liu is a professor with the School of Electronic and Information Engineering, Beihang University, China. He obtained a PhD in control science and engineering from Xi’an Jiaotong University, China in 2000. His main research interests include communication, computer network, and complex network.

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Lu, Y., Wang, F., Luo, X. et al. Novel infrared and visible image fusion method based on independent component analysis. Front. Comput. Sci. 8, 243–254 (2014). https://doi.org/10.1007/s11704-014-2328-2

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  • DOI: https://doi.org/10.1007/s11704-014-2328-2

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