Novel Face Hallucination Through Patch Position Based Multiple Regressors Fusion

摘要：

　　The task of face hallucination is to estimate one high-resolution(HR)face image from the given low-resolution(LR)one through the learning based approach.In this paper,a novel local regression learning based face hallucination is proposed.The proposed framework has two phases.In the training phase,after the training samples is separated into several clusters at each face position,the Partial Least Squares(PLS)method is used to project the original space onto a uniform manifold feature space and multiple linear regression are learned in each cluster.In the prediction phase,once the cluster of the LR patch is gotten,the corresponding learned regression function can be used to estimate HR patch.Furthermore,a multi-regressors fusion model and HR induced clustering strategy are proposed to further improve the reconstruction quality.Experiment results show that the proposed method has a very competitive performance compared with other leading algorithm with low complexity.

关键词： Face hallucination Local regression Partial Least Squares Multi-regressors fusion HR induced clustering

作者: Changkai Jiao Zongliang Gan Lina Qi Changhong Chen Feng Liu

作者单位: Jiangsu Provincial Key Lab of Image Processing and Image Communication,Nanjing University of Posts and Telecommunications,Nanjing 210003,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 369-382

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）