A hybrid parallel projection approach to object-based image restoration
Pattern Recognition Letters
A two-step neural-network based algorithm for fast image super-resolution
Image and Vision Computing
Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
Image Magnification Based on the Properties of Human Visual Processing
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Image Magnification by a Compact Method with Preservation of Preferential Components
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Sharpness preserving image enlargement by using self-decomposed codebook and Mahalanobis distance
Image and Vision Computing
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
Face Image Enhancement via Principal Component Analysis
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A single-frame super-resolution innovative approach
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Color image interpolation combined with rough sets theory
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Image enlargement by applying coordinate rotation and kernel stretching to interpolation kernels
EURASIP Journal on Advances in Signal Processing
High-zoom video hallucination by exploiting spatio-temporal regularities
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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An adaptive two-step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach