Paper Title:
Wavelet Image Denoising by Threshold Optimization Based on Genetic Algorithm
  Abstract

This paper presents a wavelet image denoising method by Threshold optimal based on wavelet transform and genetic algorithm (GA). First, using wavelet transition to a original signal and selecting a wavelet and a level of wavelet decomposition, Then the optimized thresholds of every level of wavelet decomposition will be obtained by genetic algorithms. The high coefficients at every level will be quantized. At last, inverse transition of the coefficients will be processed and we will get the final signals. An optimal image threshold using Genetic Algorithm is proposed. Compared with traditional threshold methods, the proposed method has advantages that it can implement quickly optimal threshold and have good capability and stabilization. The results show that using the proposed method can obtain satisfactory denoising effect.

  Info
Periodical
Edited by
Wenya Tian and Linli Xu
Pages
337-341
DOI
10.4028/www.scientific.net/AMR.186.337
Citation
S. P. Zhao, X. W. Li, J. H. Xing, Y. W. Ye, "Wavelet Image Denoising by Threshold Optimization Based on Genetic Algorithm", Advanced Materials Research, Vol. 186, pp. 337-341, 2011
Online since
January 2011
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