Kernel Resolution Synthesis for Superresolution
This work considers a combination classification-regression based framework with the proposal of using learned kernels in modified support vector regression to provide superresolution. The usage of both generative and discriminative learning techniques is examined first by assuming a distribution for image content for classification and then providing regression via semi-definite programming (SDP) and quadratically constrained quadratic programming (QCQP) problems. The advantage of the proposed method over other learning-based superresolution algorithms include reduced problem complexity, specificity with regard to image content, added degrees of freedom from the nonlinear approach, and excellent generalization that a combined methodology has over its individual counterparts.