@article{springerlink:10.1007/s10851-012-0368-5,
  abstract = {In this paper we present a spatially-adaptive method for image reconstruction that is based on the concept of statistical multiresolution estimation as introduced in Frick et al. (Electron. J. Stat. 6:231–268, 2012 ). It constitutes a variational regularization technique that uses an ℓ ∞ -type distance measure as data-fidelity combined with a convex cost functional. The resulting convex optimization problem is approached by a combination of an inexact alternating direction method of multipliers and Dykstra’s projection algorithm. We describe a novel method for balancing data-fit and regularity that is fully automatic and allows for a sound statistical interpretation. The performance of our estimation approach is studied for various problems in imaging. Among others, this includes deconvolution problems that arise in Poisson nanoscale fluorescence microscopy.},
  added-at = {2012-09-18T12:51:53.000+0200},
  affiliation = {Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstraße 7, 37077 Göttingen, Germany},
  author = {Frick, Klaus and Marnitz, Philipp and Munk, Axel},
  biburl = {https://www.bibsonomy.org/bibtex/24aaea0f12e125e9a58c2241244cd7753/for916},
  doi = {10.1007/s10851-012-0368-5},
  interhash = {2d339ceebefeae75dbb71df6900f5606},
  intrahash = {4aaea0f12e125e9a58c2241244cd7753},
  issn = {0924-9907},
  journal = {Journal of Mathematical Imaging and Vision},
  keyword = {Computer Science},
  keywords = {B3 Z},
  pages = {1-18},
  publisher = {Springer Netherlands},
  timestamp = {2014-02-26T21:25:57.000+0100},
  title = {Statistical Multiresolution Estimation for Variational Imaging: With an Application in Poisson-Biophotonics},
  url = {http://dx.doi.org/10.1007/s10851-012-0368-5},
  year = 2012
}