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Wavelet-Based Multi-Channel Image Denoising Using Fuzzy Logic

  • Jamal Saeedi
  • Ali Abedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic for multi-channel image denoising. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. Besides this fuzzy feature, we use inter-relation between different channels for improving the denoising performance compared to denoising each channel, separately. Then, we use the Takagi-Sugeno model based on two fuzzy features for shrinking wavelet coefficients. We examine our multi-channel image denoising algorithm in the dual-tree discrete wavelet transform domain, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithms indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.

Keywords

Dual-tree discrete wavelet transform Fuzzy membership function Multi-channel image 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jamal Saeedi
    • 1
  • Ali Abedi
    • 1
  1. 1.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran

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