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; ZhangXuesong; JiangJing; PengSilong,
Source Publication计算机辅助设计与图形学学报
Volume20(7) (EI)Issue:2008年07期Pages:856-863
Other AbstractThe size of training set as well as the usage thereof is an important issue of learning—based super—resolution.This work presents an adaptive learning method for face hallucination using Locality Preserving Projection(LPP).LPP is an efficient manifold learning method that can be used to analyze the local intrinsic features on the manifold 0f local facial areas by virtue of its ability to reveal nonlinear structures hidden in the high—dimensional image space.We fulfilled the adaptive sample selection by searching out patches online in the LPP sub-space,which makes the resultant training set tailored to the testing patch,and then effectively restored the lost high—frequency components of the lowresolution face image by patched-based eigen transformation using the dynamic training set.The experimental results fully demonstrate that the proposed method can achieve good super—resolution reconstruction performance by utilizing a relative small amount of samples.
Keyword人脸图像 / 超分辨率 / 局部保持投影 / 流形学习 / 非监督学习
Document Type期刊论文
Corresponding Author张雪松
Recommended Citation
GB/T 7714
张雪松,江静,彭思龙,等. 人脸图像超分辨率的自适应流形学习方法[J]. 计算机辅助设计与图形学学报,2008,20(7) (EI)(2008年07期):856-863.
APA 张雪松,江静,彭思龙,ZhangXuesong,JiangJing,&PengSilong,.(2008).人脸图像超分辨率的自适应流形学习方法.计算机辅助设计与图形学学报,20(7) (EI)(2008年07期),856-863.
MLA 张雪松,et al."人脸图像超分辨率的自适应流形学习方法".计算机辅助设计与图形学学报 20(7) (EI).2008年07期(2008):856-863.
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