Face super-resolution using a hybrid model
Face super-resolution is to synthesize a highresolution facial image from a low-resolution input,which can significantly improve the recognition for computer and human.In this paper,we propose a new method of super-resolution based on hybrid model including a linear model of eigenface super-resolution and a Bayesian formulation model.Principal Component Analysis (PCA) is used to approximately represent the input face image by linear combination of limited eigenface images.Then preliminary estimation of super-resolution result can be given by hallucinating the low-resolution eigenface images in the linear combination representation respectively.Finally,we use a Bayesian estimation algorithm to consider of the effect brought by subspace representation error and observation noise.Our method is demonstrated by extensive experiments with promising results of high-quality hallucinated results.
Liu Li YiDing Wang
Graduate University of Chinese Academy of Sciences,Beijing,100049,China North China University of Technology,Beijing,100041,China
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）