Header logo is

Kernel Hebbian Algorithm for single-frame super-resolution


Conference Paper


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


  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},
  month_numeric = {5}