Super-Resolution for Surveillance Facial Images via Shape Prior and Residue Compensation

Super-resolution (SR) methods are widely used to enhance the resolution of input images such as low-resolution (LR) remote or surveillance images. The key problem of SR algorithm is to introduce a proper prior for high-resolution (HR) image reconstruction. Usually pixel feature based similarity measure is used as prior. While in real surveillance applications, images are often disturbed by noise so that it is not enough to only use the prior from pixel domain to achieve satisfied high-resolution. In this paper, a two-phase based face hallucination approach via shape prior (SP) is proposed. Firstly, the Active Appearance Model (AAM) is used as shape prior to reconstruct the global face. Then the high frequency information as detailed inartificial facial features of HR face image is compensated to the global face. Experiments show that the proposed algorithm improves the subjective and objective quality of the input LR facial images and outperform many states-of-the- art superresolution methods.