Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
The FERET Evaluation Methodology for Face-Recognition Algorithms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Inpainting and Zooming Using Sparse Representations
The Computer Journal
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A multi-frame image super-resolution method
Signal Processing
Hallucinating face by position-patch
Pattern Recognition
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Multiframe super-resolution reconstruction of small moving objects
IEEE Transactions on Image Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Information Theory
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images
IEEE Transactions on Image Processing
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In compressed sensing theory, decomposing a signal based upon redundant dictionaries is of considerable interest for data representation in signal processing. The signal is approximated by an over-complete dictionary instead of an orthonormal basis for adaptive sparse image decompositions. Existing sparsity-based super-resolution methods commonly train all atoms to construct only a single dictionary for super-resolution. However, this approach results in low precision of reconstruction. Furthermore, the process of generating such dictionary usually involves a huge computational cost. This paper proposes a sparse representation and position prior based face hallucination method for single face image super-resolution. The high- and low-resolution atoms for the first time are classified to form local dictionaries according to the different regions of human face, instead of generating a single global dictionary. Different local dictionaries are used to hallucinate the corresponding regions of face. The patches of the low-resolution face inputs are approximated respectively by a sparse linear combination of the atoms in the corresponding over-complete dictionaries. The sparse coefficients are then obtained to generate high-resolution data under the constraint of the position prior of face. Experimental results illustrate that the proposed method can hallucinate face images of higher quality with a lower computational cost compared to other existing methods.