Infrared and Visible Image Fusion Based on NSCT and Deep Learning


Xin Feng, Journal of Information Processing Systems Vol. 14, No. 6, pp. 1405-1419, Dec. 2018  

https://doi.org/10.3745/JIPS.04.0096
Keywords: Boltzmann Machine, Depth Model, Image Fusion, Split Bregman Iterative Algorithm
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Abstract

An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.


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Cite this article
[APA Style]
Feng, X. (2018). Infrared and Visible Image Fusion Based on NSCT and Deep Learning. Journal of Information Processing Systems, 14(6), 1405-1419. DOI: 10.3745/JIPS.04.0096.

[IEEE Style]
X. Feng, "Infrared and Visible Image Fusion Based on NSCT and Deep Learning," Journal of Information Processing Systems, vol. 14, no. 6, pp. 1405-1419, 2018. DOI: 10.3745/JIPS.04.0096.

[ACM Style]
Xin Feng. 2018. Infrared and Visible Image Fusion Based on NSCT and Deep Learning. Journal of Information Processing Systems, 14, 6, (2018), 1405-1419. DOI: 10.3745/JIPS.04.0096.