Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Hallucinating face by position-patch
Pattern Recognition
Nonlocal back-projection for adaptive image enlargement
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
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In this paper, we present a new face image super-resolution framework using the sparse representation (SR). Firstly, a mapping function between the embedding geometries in respective image space is estimated from a training set. For super-resolution, we first seek a sparse representation for each low-resolution (LR) input, and then the representation coefficients are mapped to generate the corresponding representation coefficients in high-resolution (HR) space. Finally, the mapped coefficients are used to reconstruct the initial estimation of the target HR image. To obtain the HR images with higher fidelity, the maximum a posteriori (MAP) formulation is introduced. The effectiveness of the proposed method is evaluated through the experiments on the benchmark face database, and the experimental results demonstrate that the proposed method can achieve competitive performance compared with other state-of-the-art methods.