On debiasing restoration algorithms: applications to total-variation and nonlocal-means

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.
Type de document :
Communication dans un congrès
Scale Space and Variational Methods in Computer Vision 2015, May 2015, Lège Cap Ferret, France
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https://hal.archives-ouvertes.fr/hal-01123542
Contributeur : Charles-Alban Deledalle <>
Soumis le : jeudi 5 mars 2015 - 10:13:25
Dernière modification le : samedi 18 février 2017 - 01:17:30
Document(s) archivé(s) le : samedi 6 juin 2015 - 10:20:14

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  • HAL Id : hal-01123542, version 1
  • ARXIV : 1503.01587

Citation

Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon. On debiasing restoration algorithms: applications to total-variation and nonlocal-means. Scale Space and Variational Methods in Computer Vision 2015, May 2015, Lège Cap Ferret, France. <hal-01123542>

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