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ICASSP
2007
IEEE

Kernel Resolution Synthesis for Superresolution

14 years 6 months ago
Kernel Resolution Synthesis for Superresolution
Abstract— This work considers a combination classificationregression 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.
Karl S. Ni, Truong Nguyen
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where ICASSP
Authors Karl S. Ni, Truong Nguyen
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