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...

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Karl S. Ni, Truong Q. Nguyen
• Keywords: Color Image Patch, Examined Regression, ICIP 2007, Image Processing, Regression Portion, Single Regression, Support Vector Regression