Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
MM '08 Proceedings of the 16th ACM international conference on Multimedia
IEICE - Transactions on Information and Systems
Visio-lization: generating novel facial images
ACM SIGGRAPH 2009 papers
Bayesian tensor inference for sketch-based facial photo hallucination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face Image Enhancement via Principal Component Analysis
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Hallucinating face by position-patch
Pattern Recognition
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Directionally adaptive single frame image super resolution
International Journal of Innovative Computing and Applications
Edge-preserving single image super-resolution
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Video-based facial expression hallucination: a two- level hierarchical fusion approach
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Facial expression hallucination through eigen-associative learning
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
Spatio-temporal embedding for statistical face recognition from video
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Human face super-resolution based on NSCT
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by Locally Linear Embedding, we develop an enhanced multilinear patch hallucination algorithm, whichefficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the Coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.