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Kernel Hebbian Algorithm for single-frame super-resolution

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84014

Kim,  KI
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

Franz,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Kim, K., Franz, M., & Schölkopf, B. (2004). Kernel Hebbian Algorithm for single-frame super-resolution. In ECCV 2004 Workshop on Statistical Learning in Computer Vision (SLCV 2004) (pp. 135-149).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D951-6
Zusammenfassung
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.