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An Edge Preserving Regularization Model for Image Restoration Based on Hopfield Neural Network

  • Jian Sun
  • Zongben Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper designs an edge preserving regularization model for image restoration. First, we propose a generalized form of Digitized Total Variation (DTV), and then introduce it into restoration model as the regularization term. To minimize the proposed model, we map digital image onto network, and then develop energy descending schemes based on Hopfield neural network. Experiments show that our model can significantly better preserve the edges of image compared with the commonly used Laplacian regularization (with constant and adaptive coefficient). We also study the effects of neighborhood and gaussian parameter on the proposed model through experiments.

Keywords

Regularization Term Image Restoration Minimization Algorithm Image Denoising Hopfield Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Sun
    • 1
  • Zongben Xu
    • 1
  1. 1.Institute for Information and System ScienceXi’an Jiaotong UniversityXi’anChina

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