Title :
Face image super resolution by linear transformation
Author :
Huang, Hua ; Wu, Ning ; Fan, Xin ; Qi, Chun
Author_Institution :
Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
A novel two-step super-resolution (SR) method for face images is proposed in this paper. The critical issue of global face reconstruction in the two-step SR framework is to construct the relationship between high resolution (HR) and low resolution (LR) features. We choose the Principal Component Analysis (PCA) coefficients of LR/HR face images as the features for global faces. These features are considered as inputs and outputs of an unknown linear system. The mapping between the inputs and outputs is estimated from training sets as the system response. The HR features corresponding to a test LR image can be obtained by applying the learnt mapping to the LR features, and hence we can reconstruct the global face. Ultimately, an HR face image is generated by using the patch-based neighbor reconstruction that imposes facial details into the global face. Experiments indicate that our method produces HR faces of higher quality and is easier to implement than traditional methods based on two-step framework.
Keywords :
face recognition; feature extraction; image reconstruction; image resolution; principal component analysis; PCA; face image super resolution; face reconstruction; high resolution feature; linear transformation; low resolution feature; patch-based neighbor reconstruction; principal component analysis; two-step SR method; two-step super-resolution method; Face; Image reconstruction; Image resolution; PSNR; Principal component analysis; Strontium; Training; face Super Resolution; linear mapping;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5651255