PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Hebbian Algorithm for single-frame super-resolution
K. I. Kim, Matthias Franz and Bernhard Schölkopf
In: Statistical Learning in Computer Vision (SLCV 2004), 15 May 2004, Prague, Czek Republic.

Abstract

This paper presents a method for single-frame image super-resolution using an unsupervised learning technique. The required prior knowledge about the high-resolution images is obtained from Kernel Principal Component Analysis (KPCA). The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the {em Kernel Hebbian Algorithm}. By kernelizing the Generalized Hebbian Algorithm, one can iteratively estimate the Kernel Principal Components with only linear order memory complexity. The resulting super-resolution algorithm shows a comparable performance to the existing supervised methods on images containing faces and natural scenes.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
ID Code:376
Deposited By:Matthias Franz
Deposited On:18 December 2004