Virtual Avatar Enhanced Nonverbal Communication from Mobile Phones to PCs
Edutainment '08 Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment
Joint Blind Super-Resolution and Shadow Removing
IEICE - Transactions on Information and Systems
Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
Resolution enhancement based on learning the sparse association of image patches
Pattern Recognition Letters
Resolution-invariant image representation for content-based zooming
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Local feature hashing for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Compressive image super-resolution
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Fast covariance computation and dimensionality reduction for sub-window features in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Colorization for single image super resolution
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Video super-resolution using motion compensation and classification-aided fusion
Proceedings of the 24th Spring Conference on Computer Graphics
Locally affine patch mapping and global refinement for image super-resolution
Pattern Recognition
Edge-preserving single image super-resolution
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Single image super-resolution based on space structure learning
Pattern Recognition Letters
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In this paper, a novel method for learning based image super resolution (SR) is presented. The basic idea is to bridge the gap between a set of low resolution (LR) images and the corresponding high resolution (HR) image using both the SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic framework. We show that in this framework, the estimation of the LR image formation parameters is straightforward. The whole framework is implemented via an annealed Gibbs sampling method. Experiments on SR on both single image and image sequence input show that the proposed method provides an automatic and stable way to compute super-resolution and the achieved result is encouraging for both synthetic and real LR images.