A parameter study of a hybrid Laplacian mean-curvature flow denoising model

  • Authors:

  • Ioana Banicescu;Hyeona Lim;Ricolindo L. Cariño;Seongjai Kim

  • Affiliations:

  • Department of Computer Science and Engineering, and Center for Computational Sciences--HPCC, Mississippi State University, Mississippi State, USA 39762;Department of Mathematics and Statistics, Mississippi State University, Mississippi State, USA 39762-5921;Center for Advanced Vehicular Systems---HPCC, Mississippi State University, Mississippi State, USA 39762;Department of Mathematics and Statistics, Mississippi State University, Mississippi State, USA 39762-5921

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2011

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Abstract

This article presents results of a parameter study for a new denoising model, using parallel computing and advanced dynamic load balancing techniques for performance improvement of implementations. A denoising model is suggested hybridizing total variation and Laplacian mean-curvature; the fourth-order model and its numerical procedure introduce a number of parameters. As a preliminary step in the model development, a parameter study has been undertaken in order to discover solitary and interactive effects of the parameters on model accuracy. Such a parameter study is necessarily time-consuming due to the huge number of combinations of the parameter values to be tested. In addition, the study has to be performed on various images, thereby increasing the overall investigation time. The performance of this first parallel implementation of a new hybrid model for image denoising is evaluated when the application is running on heterogeneous environments. The hybrid model is simulated on a general-purpose Linux cluster for which the parallel efficiency exceeds 96%.