Empirical Inference Conference Paper 2010

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

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.

Author(s): Harmeling, S. and Sra, S. and Hirsch, M. and Schölkopf, B.
Journal: Proceedings of the 17th International Conference on Image Processing (ICIP 2010)
Pages: 3313-3316
Year: 2010
Month: September
Day: 0
Publisher: IEEE
Bibtex Type: Conference Paper (inproceedings)
Address: Piscataway, NJ, USA
DOI: 10.1109/ICIP.2010.5651650
Event Name: 17th International Conference on Image Processing (ICIP 2010)
Event Place: Hong Kong, China
Digital: 0
Electronic Archiving: grant_archive
Institution: Institute of Electrical and Electronics Engineers
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex

@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},
  slug = {6673},
  author = {Harmeling, S. and Sra, S. and Hirsch, M. and Sch{\"o}lkopf, B.},
  month_numeric = {9}
}