Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM

@inproceedings{6673,
  title = {Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM},
  journal = {Proceedings of the 17th International Conference on Image Processing (ICIP 2010)},
  abstract = {We formulate the multiframe blind deconvolution problem in an incremental
  expectation maximization (EM) framework. Beyond deconvolution,
  we show how to use the same framework to address: (i)
  super-resolution despite noise and unknown blurring; (ii) saturationcorrection
  of overexposed pixels that confound image restoration.
  The abundance of data allows us to address both of these without
  using explicit image or blur priors. The end result is a simple but effective
  algorithm with no hyperparameters. We apply this algorithm
  to real-world images from astronomy and to super resolution tasks:
  for both, our algorithm yields increased resolution and deconvolved
  images simultaneously.},
  pages = {3313-3316},
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  institution = {Institute of Electrical and Electronics Engineers},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2010},
  author = {Harmeling, S. and Sra, S. and Hirsch, M. and Sch{\"o}lkopf, B.},
  doi = {10.1109/ICIP.2010.5651650},
  month_numeric = {9}
}