International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia
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
An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images
IEEE Transactions on Image Processing
Frontal face generation from multiple low-resolution non-frontal faces for face recognition
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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Principal Component Analysis (PCA) is commonly used for facial images representation in global face super-resolution But the features extracted by PCA are holistic and difficult to have semantic interpretation For synthesizing a better super-resolution result, we introduce non-negative matrix factorization (NMF) to extract face features, and enhance semantic (non-negative) information of basis images Furthermore, for improving the quality of super-resolution facial image which has been deteriorated by strong noise, we propose a global face super resolution with contour region constraints (CRNMF), which maks use of the differences of face contour region in gray value as face similarity function Because the contours of the human face contain the structural information, this method preserves face structure similarity and reduces dependence on the pixels Experimental results show that the NMF-based face super-resolution algorithm performs better than PCA-based algorithms and the CRNMF-based face super-resolution algorithm performs better than NMF-based under the noisy situations.