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 classification-regression 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 semi definite programming (SDP) and quadratically constrained quadratic programming (QCQP) problem. The SVR is further modified by scaling the training points in the SDP and QCQP problems by their relevance and importance to the examined regression. The result is a weighted average of different regressions depending on how much a single regression is likely to contribute, where advantages include reduced problem complexity, specificity with regard to image content, added degrees of freedom from a nonlinear approaches, and excellent generalization that a combined methodology has over its individual counterparts.