KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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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)
  • Received : 2017.02.16
  • Accepted : 2017.05.28
  • Published : 2017.10.31
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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.

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References

  1. Tarek Elguebaly and Nizar Bouguila, "Finite asymmetric generalized Gaussian mixture models learning for infrared object detection," Computer Vision & Image Understanding, vol. 117, no. 12, pp. 1659-1671, December, 2013. https://doi.org/10.1016/j.cviu.2013.07.007
  2. Juan Su, Luo Yang and Jun Lu, "An Infrared Target Detection Algorithm Based on Knowledge Model," Acta Photonica Sinica, vol. 42, no. 7, pp. 839-844, July 2013. https://doi.org/10.3788/gzxb20134207.0839
  3. Zhaohui Li, Bing Wang and Ming Chen, "Infrared Target Detection Based on Wavelet Multi Scale and Maximum Likelihood," Acta Optica Sinica, vol. 29, no. 12, pp. 3404-3408, December 2009. https://doi.org/10.3788/AOS20092912.3404
  4. He Deng and Jianguo Liu, "Infrared small target detection based on the self-information map," Infrared Physics & Technology, vol. 54, no. 2, pp. 100-107, March 2011. https://doi.org/10.1016/j.infrared.2011.01.003
  5. Xiujie Qu, He Chen and Guihua Peng, "Novel detection method for infrared small targets using weighted information entropy," Journal of Systems engineering and electronics, vol. 23, no.6, pp. 838-842, December 2012. https://doi.org/10.1109/JSEE.2012.00102
  6. Tae-Wuk Bae, "Small target detection using bilateral filter and temporal cross product in infrared images," Infrared Physics & Technology, vol. 54, no. 5, pp. 403-411, September 2011. https://doi.org/10.1016/j.infrared.2011.06.006
  7. Peng Lu, Shanshan Zhang, Chi Liu, Shilei Huang and Yuhe Tang, "Object detection algorithm based on sparse overcomplete representation," Chinese Journal of Scientific Instrument, vol. 34, no. 6, pp. 1273-1278, June, 2013.
  8. Yuanrong Tian, Song Tian and Yuelei Xu, "Image object detection based on local feature and sparse representation," Journal of Computer Applications, vol. 33, no. 6, pp. 1670-1673, June 2013. https://doi.org/10.3724/SP.J.1087.2013.01670
  9. Jiajia Zhao, Zhengyuan Tang and Jie Yang, "Infrared small target detection based on image sparse representation," Journal of Infrared & Millimeter Waves, vol. 30, no. 2, pp. 156-161, February 2012. https://doi.org/10.3724/SP.J.1010.2011.00156
  10. Yujie He, Min Li and Jinli Zhang, "Small infrared target detection based on low-rank and sparse representation," Infrared Physics & Technology, vol. 68, pp. 98-109, January 2015. https://doi.org/10.1016/j.infrared.2014.10.022
  11. Michael. Elad and Michal Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736-3745, December 2006. https://doi.org/10.1109/TIP.2006.881969
  12. Jian Zhang, Debin Zhao and Wen Gao, "Group-Based Sparse Representation for Image Restoration," IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3336-3351, March, 2014. https://doi.org/10.1109/TIP.2014.2323127
  13. Xin Wang, Siqiu Shen, Chen Ning, Fengchen Huang and Hongmin Gao, "Multi-class remote sensing object recognition based on discriminative sparse representation," Applied Optics, vol. 55, no. 6, pp. 1381-1394, June, 2016. https://doi.org/10.1364/AO.55.001381
  14. Zhenfeng Zhu, Qian Chen, Yao Zhao, "Ensemble dictionary learning for saliency detection," Image & Vision Computing, vol. 32, no. 3, pp. 180-188, March, 2014. https://doi.org/10.1016/j.imavis.2013.12.015
  15. Tianzhu Zhang, Si Liu, Changsheng Xu, Shuicheng Yan, Bernard Ghanem, Narendra Ahuja, and Ming-Hsuan Yang, "Structural Sparse Tracking," in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pp.150-158, June 7-12, 2015.
  16. Tianzhu Zhang, Si Liu, Narendra Ahuja, Ming-Hsuan Yang, and Bernard Ghanem, "Robust Visual Tracking Via Consistent Low-Rank Sparse Learning, " International Journal of Computer Vision, vol. 111, no. 2, pp. 171-190, January, 2015. https://doi.org/10.1007/s11263-014-0738-0
  17. Tianzhu Zhang, Bernard Ghanem, Si Liu, "Low-Rank Sparse Coding for Image Classification," in Proc. of IEEE International Conference on Computer Vision, pp. 281-288, December 1-8, 2013.
  18. Xiaoshan Yang, Tianzhu Zhang and Changsheng Xu, "Locality discriminative coding for image classification," in Proc. of International Conference on Internet Multimedia Computing and Service, pp.52-55, August 17-19, 2013.
  19. Xiaoshan Yang, Tianzhu Zhang, Changsheng Xu, and Min Xu, "Graph-Guided Fusion Penalty Based Sparse Coding for Image Classification," in Proc. of Pacific-Rim Conference on Advances in Multimedia Information Processing, pp. 475-484, December 13-16, 2013.
  20. Zhuolin Jiang, Zhe Lin and Larry S. Davis, "Label consistent K-SVD: learning a discriminative dictionary for recognition," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 35, pp. 2651-2664, November 2013.. https://doi.org/10.1109/TPAMI.2013.88
  21. Michal Aharon, Michael Elad and Alfed Bruckstein, "K-svd: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, October, 2006. https://doi.org/10.1109/TSP.2006.881199
  22. Tanaya Guha and Rabab K. Ward, "Learning sparse representations for human action recognition," IEEE Transactions on Software Engineering, vol. 34, no. 8, pp. 1576-1588, December, 2011.
  23. Xin. Wang, Guofang Lv and Lizhong Xu, "Infrared dim target detection based on visual attention," Infrared Physics & Technology, vol. 55, no. 6, pp. 513-521, June 2012. https://doi.org/10.1016/j.infrared.2012.08.004
  24. Wenguang Wang, Chenming Li and Jianing Shi, "A robust infrared dim target detection method based on template filtering and saliency extraction," Infrared Physics & Technology, vol. 73, pp. 19-28, November 2015. https://doi.org/10.1016/j.infrared.2015.08.015
  25. Xin Wang, Chen Ning and Lizhong Xu, "Spatiotemporal saliency model for small moving object detection in infrared videos," Infrared Physics & Technology, vol. 69, pp. 111-117, January 2015. https://doi.org/10.1016/j.infrared.2015.01.018
  26. J. W. Davis, OTCBVS Benchmark Dataset Collection, http://vcipl-okstate.org/pbvs/bench/
  27. Yansheng Li, Yihua Tan, Hang Li and Jinwen Tian, "Biologically inspired multilevel approach for multiple moving targets detection from airborne forward-looking infrared sequences," Journal of the Optical Society of America A, vol. 31, no. 4, pp. 734-44, April, 2014. https://doi.org/10.1364/JOSAA.31.000734
  28. Chen Zheng, Hong Zheng, Qiangqiang Sui and Xiangzhen Li, "An efficient background prediction algorithm for infrared dim target detection," in Proc. of IEEE International Conference on Natural Computation, pp. 89-91, August 10-12, 2010.
  29. Xiangzhi Bai, Fugen Zhou, "Infrared small target enhancement and detection based on modified top-hat transformations," Computers & Electrical Engineering, vol. 36, no. 6, pp. 1193-1201, November, 2010. https://doi.org/10.1016/j.compeleceng.2010.05.008
  30. Feng Jiang, Minhui, Jin and Liquan Song, "Small infrared target detection algorithm based on mathematical morphology," in Proc. of SPIE, pp. 81934L-81934L-7, September 8, 2011.
  31. C. L. Philip Chen, Hong Li, Yantao Wei, Tian Xia and Yuan Yan Tang, "A local contrast method for small infrared target detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 574-581, January 2014. https://doi.org/10.1109/TGRS.2013.2242477
  32. Xiaoshan Yang, Tianzhu Zhang, and ChangshengXu, "Cross-Domain Feature Learning in Multimedia," IEEE Transactions on Multimedia, vol.17, no.1, pp. 64-78, December 2014. https://doi.org/10.1109/TMM.2014.2375793
  33. Jianbing Shen, Xiaoshan Yang, Yunde Jia, and Xuelong Li, "Intrinsic images using optimization," in Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3481-3487, August, 2011.