CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
张雪松; 江静; 彭思龙,
Source Publication计算机辅助设计与图形学学报,
Volume22(3) (EI)Issue:2010年03期Pages:487-493
Other AbstractRegularization in the conventional SR process can help to gain a numerical stability and constrain the smoothness of solutions.However,this does not necessarily promise a high quality reconstruction result.This paper proposes a new regularization method for facial image SR,eigensubspace based regularization.Looking upon the patches of face images as some specific class of signals,their eigen-subspace can be found by Principal Components Analysis,and the super-resolved facial patches are regularized in the orthogonal complement of the eigen-subspace.This eigen-subspace based regularization is combined with the classic reconstruction constraint under a Bayesian framework to produce the high—resolution outcome.Another contribution of this work is we present three iterative algorithms of j oint registration and reconstruction estimation,using an affine motion model rather than the most adopted pure translational model.The experimental results illustrate the effectiveness of our approach.
Keyword超分辨率 / 人脸图像 / 规整化 / 图像配准 / 特征子空间
Document Type期刊论文
Corresponding Author张雪松
Recommended Citation
GB/T 7714
张雪松,江静,彭思龙,. 特征子空间规整化的人脸图像超分辨率重建[J]. 计算机辅助设计与图形学学报,,2010,22(3) (EI)(2010年03期):487-493.
APA 张雪松,江静,&彭思龙,.(2010).特征子空间规整化的人脸图像超分辨率重建.计算机辅助设计与图形学学报,,22(3) (EI)(2010年03期),487-493.
MLA 张雪松,et al."特征子空间规整化的人脸图像超分辨率重建".计算机辅助设计与图形学学报, 22(3) (EI).2010年03期(2010):487-493.
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