Infrared and Visible Image Fusion Based on NSCT and Deep Learning

Xin Feng
Volume: 14, No: 6, Page: 1405 ~ 1419, Year: 2018
Keywords: Boltzmann Machine, Depth Model, Image Fusion, Split Bregman Iterative Algorithm
Full Text:

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.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.

Cite this article
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.