Special Section on Video Surveillance and Transportation Imaging Applications

Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

[+] Author Affiliations
Junjun Jiang

Wuhan University, National Engineering Research Center for Multimedia Software, School of Computer, Wuhan 430072, China

Ruimin Hu

Wuhan University, National Engineering Research Center for Multimedia Software, School of Computer, Wuhan 430072, China

Zhen Han

Wuhan University, National Engineering Research Center for Multimedia Software, School of Computer, Wuhan 430072, China

Zhongyuan Wang

Wuhan University, National Engineering Research Center for Multimedia Software, School of Computer, Wuhan 430072, China

Jun Chen

Wuhan University, National Engineering Research Center for Multimedia Software, School of Computer, Wuhan 430072, China

J. Electron. Imaging. 22(4), 041120 (Oct 08, 2013). doi:10.1117/1.JEI.22.4.041120
History: Received April 17, 2013; Revised August 29, 2013; Accepted September 11, 2013
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Abstract.  Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

© 2013 SPIE and IS&T

Citation

Junjun Jiang ; Ruimin Hu ; Zhen Han ; Zhongyuan Wang and Jun Chen
"Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation", J. Electron. Imaging. 22(4), 041120 (Oct 08, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.041120


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