Example-Based Learning for Single-Image Super-Resolution
Proceedings of the 30th DAGM symposium on Pattern Recognition
Neighbor embedding based super-resolution algorithm through edge detection and feature selection
Pattern Recognition Letters
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
Sparse Bayesian learning of filters for efficient image expansion
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
New learning based super-resolution: use of DWT and IGMRF prior
IEEE Transactions on Image Processing
Learning-based super resolution using kernel partial least squares
Image and Vision Computing
Interval-valued fuzzy sets for color image super-resolution
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Image super-resolution by curve fitting in the threshold decomposition domain
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation
Morphable model space based face super-resolution reconstruction and recognition
Image and Vision Computing
Super-resolution texture synthesis using stochastic PAR/NL model
Journal of Visual Communication and Image Representation
Greedy regression in sparse coding space for single-image super-resolution
Journal of Visual Communication and Image Representation
Human face super-resolution based on NSCT
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Image super resolution using Gaussian Process Regression with patch clustering
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results