Sciweavers

Share
ICIP
2007
IEEE

Color Image Superresolution Based on a Stochastic Combinational Classification-Regression Algorithm

11 years 1 months ago
Color Image Superresolution Based on a Stochastic Combinational Classification-Regression Algorithm
Abstract - The proposed algorithm in this work provides superresolution for color images by using a learning based technique that utilizes both generative and discriminant approaches. The combination of the two approaches is designed with a stochastic classificationregression framework where a color image patch is first classified by its content, and then, based on the class of the patch, a learned regression provides the optimal solution. For good generalization, the classification portion of the algorithm determines the probability that the image patch is in a given class by modeling all possible image content (learned through a training set) as a Gaussian mixture, with each Gaussian of the mixture portraying a single class. The regression portion of the algorithm has been chosen to be a modified Support Vector Regression, where the kernel has been learned by solving a semidefinite programming (SDP) and quadratically constrained quadratic programming (QCQP) problem. The SVR is furthe...
Karl S. Ni, Truong Q. Nguyen
Added 21 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2007
Where ICIP
Authors Karl S. Ni, Truong Q. Nguyen
Comments (0)
books