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http://dx.doi.org/10.3837/tiis.2017.10.017

A novel hybrid method for robust infrared target detection  

Wang, Xin (College of Computer and Information, Hohai University)
Xu, Lingling (College of Computer and Information, Hohai University)
Zhang, Yuzhen (Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology)
Ning, Chen (School of Physics and Technology, Nanjing Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.10, 2017 , pp. 5006-5022 More about this Journal
Abstract
Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.
Keywords
Infrared; target detection; local sparse representation; contrast;
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