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On Variational Curve Smoothing and Reconstruction

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Abstract

In this paper we discuss and experimentally compare variational methods for curve denoising, curve smoothing and curve reconstruction problems. The methods are based on defining suitable cost functionals to be minimized, the cost being the combination of a fidelity term measuring the “distance” of a curve from the data and a smoothness term measuring the curve’s L 1-norm or length.

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Authors and Affiliations

  1. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Yu Wang & Desheng Wang

  2. Department of Computer Science, Technion IIT, Haifa, Israel

    A. M. Bruckstein

Authors
  1. Yu Wang
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  2. Desheng Wang
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  3. A. M. Bruckstein
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Corresponding author

Correspondence to A. M. Bruckstein.

Additional information

Professor A.M. Bruckstein’s work was supported in part by an NTU joint visiting professorship at the School of Physical and Mathematical Sciences and the Institute for Media Innovations.

The work is supported in part by the NTU start-up grant M58110011, Singapore MOE ARC 29/07 T207B2202 and NRF 2007IDM-IDM 002-010.

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Wang, Y., Wang, D. & Bruckstein, A.M. On Variational Curve Smoothing and Reconstruction. J Math Imaging Vis 37, 183–203 (2010). https://doi.org/10.1007/s10851-010-0201-y

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  • DOI: https://doi.org/10.1007/s10851-010-0201-y

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