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Journal of Sensors
Volume 2018, Article ID 5754702, 15 pages
Research Article

Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

1School of Information, Yunnan University, Kunming 650500, China
2School of Automation, Southeast University, Nanjing 210096, China

Correspondence should be addressed to Dongming Zhou; nc.ude.uny@mduohz and Xuejie Zhang; nc.ude.uny@gnahzjx

Received 14 August 2017; Revised 19 December 2017; Accepted 25 December 2017; Published 5 April 2018

Academic Editor: Calogero M. Oddo

Copyright © 2018 Kangjian He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.