Kernel Hebbian Algorithm for single-frame super-resolution
2004
Conference Paper
ei
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.
Author(s): | Kim, KI. and Franz, M. and Schölkopf, B. |
Book Title: | Computer Vision - ECCV 2004, LNCS vol. 3024 |
Journal: | Statistical Learning in Computer Vision (SLCV 2004) |
Pages: | 135-149 |
Year: | 2004 |
Month: | May |
Day: | 0 |
Editors: | A Leonardis and H Bischof |
Publisher: | Springer |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 8th European Conference on Computer Vision (ECCV 2004) |
Event Place: | Praha, Czech Republic |
Address: | Berlin, Germany |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
Web |
BibTex @inproceedings{2645, title = {Kernel Hebbian Algorithm for single-frame super-resolution}, author = {Kim, KI. and Franz, M. and Sch{\"o}lkopf, B.}, journal = {Statistical Learning in Computer Vision (SLCV 2004)}, booktitle = {Computer Vision - ECCV 2004, LNCS vol. 3024}, pages = {135-149}, editors = {A Leonardis and H Bischof}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = may, year = {2004}, doi = {}, month_numeric = {5} } |