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  Kernel Hebbian Algorithm for single-frame super-resolution

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D951-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-538A-2
Genre: Conference Paper

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 Creators:
Kim, KI1, 2, Author           
Franz, M1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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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 Dates: 2004-05
 Publication Status: Issued
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 Identifiers: BibTex Citekey: 2645
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Title: ECCV 2004 Workshop on Statistical Learning in Computer Vision (SLCV 2004)
Place of Event: Praha, Czech Republic
Start-/End Date: 2004-05-15

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Title: ECCV 2004 Workshop on Statistical Learning in Computer Vision (SLCV 2004)
Source Genre: Proceedings
 Creator(s):
Leonardis, A, Editor
Bischof, H, Editor
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 135 - 149 Identifier: -