On debiasing restoration algorithms: applications to total-variation and nonlocal-means (bibtex)
by C.-A. Deledalle, N. Papadakis, J. Salmon
Abstract:
Bias in image restoration algorithms can hamper further analysis, typically when the intensities have a physical meaning of interest, e.g., in medical imaging. We propose to suppress a part of the bias -- the method bias -- while leaving unchanged the other unavoidable part -- the model bias. Our debiasing technique can be used for any locally affine estimator including l1-regularization, anisotropic total-variation and some nonlocal filters.
Reference:
On debiasing restoration algorithms: applications to total-variation and nonlocal-means C.-A. Deledalle, N. Papadakis, J. Salmon, SSVM, pp. 129-141, 2015.
Bibtex Entry:
@InProceedings{Deledalle_Papadakis_Salmon15,
  Title         = {{On debiasing restoration algorithms: applications to total-variation and nonlocal-means}},
  Author        = {{C.-A.} Deledalle and N. Papadakis and J. Salmon},
  Booktitle     = {SSVM},
  Year          = {2015},
  pages         = {129-141},
  Keywords      = {restoration; bias; debiasing; l1 regularization;
                   model subspace; refitting},
  abstract      = {Bias in image restoration algorithms can hamper further
                   analysis, typically when the intensities have a physical
                   meaning of interest, e.g., in medical imaging. We propose
                   to suppress a part of the bias -- the method bias -- while
                   leaving unchanged the other unavoidable part -- the model
                   bias. Our debiasing technique can be used for any locally
                   affine estimator including l1-regularization, anisotropic
                   total-variation and some nonlocal filters.},
  Url  = {https://hal.archives-ouvertes.fr/hal-01123542/document}
}
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