Simulation of a hybrid model for image denoising

  • Authors:

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

  • Affiliations:

  • Center for Computational Sciences, Mississippi State University, Mississippi State, MS;Dept. of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, and Center for Computational Sciences, Mississippi State University, Mississippi State, MS;Dept. of Mathematics and Statistics, Mississippi State University, Mississippi State, MS;Dept. of Mathematics and Statistics, Mississippi State University, Mississippi State, MS;Dept. of Mathematics and Statistics, Mississippi State University, Mississippi State, MS

  • Venue:
  • IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
  • Year:
  • 2006

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Abstract

We propose a new model for image denoising which is a hybrid of the total variation model and the Laplacian mean-curvature model. An efficient numerical procedure to compute the hybrid model is also presented. The hybrid model and its computational procedure introduce a number of parameters. As a preliminary step to the synthesis of a method for selecting optimal parameters, the hybrid model was simulated on a number of known images with synthetically added noise. The parallel simulation code was easily composed from existing serial code and a dynamic load balancing tool. The estimated parallel efficiency of the simulation is in excess of 96% on 32 processors of a general-purpose Linux cluster.