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Kernel Hebbian algorithm for single-frame super-resolution

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Kernel Hebbian algorithm for single-frame super-resolution. / Kim, Kwang In; Franz, Matthias O.; Schölkopf, Bernhard.
Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004. ed. / A. Leonardis; H. Bischof. Tubingen, Germany: Max Planck Institute fur biologische Kybernetik, 2004. p. 135-149.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Kim, KI, Franz, MO & Schölkopf, B 2004, Kernel Hebbian algorithm for single-frame super-resolution. in A Leonardis & H Bischof (eds), Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004. Max Planck Institute fur biologische Kybernetik, Tubingen, Germany, pp. 135-149. <http://www.is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/pdf2645.pdf>

APA

Kim, K. I., Franz, M. O., & Schölkopf, B. (2004). Kernel Hebbian algorithm for single-frame super-resolution. In A. Leonardis, & H. Bischof (Eds.), Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004 (pp. 135-149). Max Planck Institute fur biologische Kybernetik. http://www.is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/pdf2645.pdf

Vancouver

Kim KI, Franz MO, Schölkopf B. Kernel Hebbian algorithm for single-frame super-resolution. In Leonardis A, Bischof H, editors, Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004. Tubingen, Germany: Max Planck Institute fur biologische Kybernetik. 2004. p. 135-149

Author

Kim, Kwang In ; Franz, Matthias O. ; Schölkopf, Bernhard. / Kernel Hebbian algorithm for single-frame super-resolution. Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004. editor / A. Leonardis ; H. Bischof. Tubingen, Germany : Max Planck Institute fur biologische Kybernetik, 2004. pp. 135-149

Bibtex

@inproceedings{deb29ff235af4949ae6f61c67d2f5c64,
title = "Kernel Hebbian algorithm for single-frame super-resolution",
abstract = "This paper presents a method for single-frame image superresolution 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 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.",
author = "Kim, {Kwang In} and Franz, {Matthias O.} and Bernhard Sch{\"o}lkopf",
year = "2004",
language = "English",
pages = "135--149",
editor = "Leonardis, {A. } and Bischof, {H. }",
booktitle = "Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004",
publisher = "Max Planck Institute fur biologische Kybernetik",

}

RIS

TY - GEN

T1 - Kernel Hebbian algorithm for single-frame super-resolution

AU - Kim, Kwang In

AU - Franz, Matthias O.

AU - Schölkopf, Bernhard

PY - 2004

Y1 - 2004

N2 - This paper presents a method for single-frame image superresolution 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 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.

AB - This paper presents a method for single-frame image superresolution 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 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.

M3 - Conference contribution/Paper

SP - 135

EP - 149

BT - Statistical Learning in Computer Vision, ECCV 2004 Workshop, Prague, Czech Republic, May 2004

A2 - Leonardis, A.

A2 - Bischof, H.

PB - Max Planck Institute fur biologische Kybernetik

CY - Tubingen, Germany

ER -