Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Digital Pictures: Representation, Compression, and Standards
Digital Pictures: Representation, Compression, and Standards
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Super-resolution of images based on local correlations
IEEE Transactions on Neural Networks
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This paper investigates face image enhancement based on the principal component analysis (PCA). We first construct two types of training samples: one consists of some high-resolution face images, and the other includes the low resolution images obtained via smoothed and down-sampling process from the first set. These two corresponding sets form two different image spaces with different resolutions. Second, utilizing the PCA, we obtain two eigenvector sets which form the vector basis for the high resolution space and the low resolution space, and a unique relationship between them is revealed. We propose the algorithm as follows: first project the low resolution inquiry image onto the low resolution image space and produce a coefficient vector, then a super-resolution image is reconstructed via utilizing the basis vector of high-resolution image space with the obtained coefficients. This method improves the visual effect significantly; the corresponding PSNR is much larger than other existing methods.