期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2012
卷号：5
期号：4
出版社：SERSC
摘要：In a learning-based super-resolution algorithm, suitable prior from the training database is a key issue. A novel face hallucination algorithm based on shape clustering and subspace learning for adaptive prior is proposed in this paper. We define face shape metrics with point distribution model by Hausdorff Distance, then a framework of adaptive prior and subspace learning is proposed to enhance the performance of surveillance face super-resolution. Linear regression is used to learn the relationship between low and high image systhesis coefficients. Experiments show that the face super-resolution algorithm based on shape classification can improve the subjective and objective quality of the input low-resolution face images and outperform many state-of-the-art global-based face super-resolution methods.
关键词：Surveillance; Super-resolution; Face shape; K-means clustering; Subspace;Learning
