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Sensors 2014, 14(6), 9451-9470; doi:10.3390/s140609451
Article

Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

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Received: 7 February 2014; in revised form: 8 May 2014 / Accepted: 14 May 2014 / Published: 27 May 2014
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Abstract: It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn’t be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.
Keywords: dim target detection; adaptive morphological over-compete dictionary; discriminative over-complete dictionary; Gaussian over-complete dictionary; sparsity dim target detection; adaptive morphological over-compete dictionary; discriminative over-complete dictionary; Gaussian over-complete dictionary; sparsity
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.

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MDPI and ACS Style

Li, Z.-Z.; Chen, J.; Hou, Q.; Fu, H.-X.; Dai, Z.; Jin, G.; Li, R.-Z.; Liu, C.-J. Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online. Sensors 2014, 14, 9451-9470.

AMA Style

Li Z-Z, Chen J, Hou Q, Fu H-X, Dai Z, Jin G, Li R-Z, Liu C-J. Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online. Sensors. 2014; 14(6):9451-9470.

Chicago/Turabian Style

Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju. 2014. "Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online." Sensors 14, no. 6: 9451-9470.


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