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

Junjun Jiang; Ruimin Hu; Zhen Han; Zhongyuan Wang; Jun Chen

doi:10.1117/1.JEI.22.4.041120

8 October 2013 Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

Junjun Jiang, Ruimin Hu, Zhen Han, Zhongyuan Wang, Jun Chen

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 041120 (October 2013). https://doi.org/10.1117/1.JEI.22.4.041120

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.

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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," Journal of Electronic Imaging 22(4), 041120 (8 October 2013). https://doi.org/10.1117/1.JEI.22.4.041120

Published: 8 October 2013

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