期刊
INFRARED PHYSICS & TECHNOLOGY
卷 71, 期 -, 页码 87-98出版社
ELSEVIER
DOI: 10.1016/j.infrared.2015.02.008
关键词
Image fusion; Non-subsampled shearlet transform; Intersecting cortical model; Artificial neural network
资金
- National Natural Science Foundations of China [61309008, 61309022]
- Natural Science Foundation of Shannxi Province of China [2013JQ8031, 2014JQ8049]
- China Postdoctoral Science Foundation [2013M532133, 2014M552718, 2014T71016]
- Foundation of Science and Technology on Information Assurance Laboratory [KJ-13-108]
- Natural Science Foundations of the Engineering University of the Armed Police Force of China [WJY-201214, WJY-201414, WJY-201312]
Fusion of infrared and visible images is an active research area in image processing, and a variety of relevant algorithms have been developed. However, the existing techniques commonly cannot gain good fusion performance and acceptable computational complexity simultaneously. This paper proposes a novel image fusion approach that integrates the non-subsampled shearlet transform (NSST) with spiking cortical model (SCM) to overcome the above drawbacks. On the one hand, using NSST to conduct the decompositions and reconstruction not only consists with human vision characteristics, but alto effectively decreases the computational complexity compared with the current popular multi-resolution analysis tools such as non-subsampled contourlet transform (NSCT). On the other hand, SCM, which has been considered to be an optimal neuron network model recently, is responsible for the fusion of sub-images from different scales and directions. Experimental results indicate that the proposed method is promising, and it does significantly improve the fusion quality in both aspects of subjective visual performance and objective comparisons compared with other current popular ones. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据