title: Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.
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.
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, China. lizhengzhou@cqu.edu.cn.
authors.forename: Zheng-Zhou
authors.initials: ZZ
authors.lastname: Li
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, China. chenjingsunshing@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Chen
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, China. shuijingxin@gmail.com.
authors.forename: Qian
authors.initials: Q
authors.lastname: Hou
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, China. fhxma092@gmail.com.
authors.forename: Hong-Xia
authors.initials: HX
authors.lastname: Fu
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, China. daizhenlite@gmail.com.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Dai
authors.affiliation: China Aerodynamics Research and Development Center, Mianyang 621000, China. gjin@ioe.ac.cn.
authors.forename: Gang
authors.initials: G
authors.lastname: Jin
authors.affiliation: National Laboratory of Analogue Integrated Circuits, No. 24 Research Institute of China Electronics Technology Group Corporation, Chongqing 400060, China. lrzmail@163.com.
authors.forename: Ru-Zhang
authors.initials: RZ
authors.lastname: Li
authors.affiliation: National Laboratory of Analogue Integrated Circuits, No. 24 Research Institute of China Electronics Technology Group Corporation, Chongqing 400060, China. liucj@cetccq.com.cn.
authors.forename: Chang-Ju
authors.initials: CJ
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018531:Spacecraft; D013055:Spectrophotometry, Infrared