@inproceedings{2645,
  title = {Kernel Hebbian Algorithm for single-frame super-resolution},
  journal = {Statistical Learning in Computer Vision (SLCV 2004)},
  booktitle = {Computer Vision - ECCV 2004, LNCS vol. 3024},
  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.},
  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},
  author = {Kim, KI. and Franz, M. and Sch{\"o}lkopf, B.},
  month_numeric = {5}
}