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Volume 31 Issue 5
Dec.  2010
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Wang Na, Shi Gong-Tao, Lu Jun, Kuang Gang-Yao. A New Polarimetric SAR Image CFAR Target Detecting Method[J]. Journal of Electronics & Information Technology, 2011, 33(2): 395-400. doi: 10.3724/SP.J.1146.2010.00023
Citation: Zhou Deng-wen, Liu Ke-qin. Neighborhood Adaptive Image Denoising Using Dual-Tree Complex Wavelet Transform[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1197-1200. doi: 10.3724/SP.J.1146.2008.00469

Neighborhood Adaptive Image Denoising Using Dual-Tree Complex Wavelet Transform

doi: 10.3724/SP.J.1146.2008.00469
  • Received Date: 2008-04-22
  • Rev Recd Date: 2008-09-19
  • Publish Date: 2009-05-19
  • In this paper, a new neighborhood adaptive image denoising method is proposed using dual-tree complex wavelet transforms. It is an improvement of the existing denoising method NeighShrink. The optimal thresholds and neighboring window sizes are determined for every subband in the wavelet domain using Steins unbiased risk estimate, and NeighShrink is also extended from orthogonal wavelet transforms to dual-tree complex wavelet transforms in this paper. Experimental results show that the proposed method performs?better than some of the existing methods.
  • Donoho D L and Johnstone I M. Ideal spatial adaptation viawavelet shrinkage [J].Biometrika.1994, 81(3):425-455[2]Cai T T and Silverman B W. Incorporating information onneighbouring coefficients into wavelet estimation[J]. Sankhya:The Indian Journal of Statistics, Series B, 2001, 63(2):127-148.[3]Chen G Y, Bui T D, and Krzy˙zak A. Image denoising withneighbour dependency and customized wavelet and threshold[J].Pattern Recognition.2005, 38(1):115-124[4]Coifman R R and Donoho D L. Translation invariantdenoising [C]. In Wavelets and Statistics, Springer LectureNotes in Statistics 103, San Diego, CA, USA, 1995: 125-150.[5]Selesnick I W, Baraniuk R G, and Kingsbury N G. The dualtreecomplex wavelet transform [J].IEEE Signal ProcessingMagazine.2005, 22(6):123-151[6]Balster E J, Zheng Y F, and Ewing R L. Feature-basedwavelet shrinkage algorithm for image denoising [J].IEEETrans. on Image Processing.2005, 14(12):2024-2039[7]Pizurica A and Philips W. Estimating probability of presenceof a signal of interest in multiresolution single- and multibandimage denoising [J].IEEE Trans. on Image Processing.2006,15(3):654-665[8]Blu T and Luisier F. The SURE-LET approach to imagedenoising [J].IEEE Trans. on Image Processing.2007, 16(11):2778-2786[9]Sendur L and Selesnick I W. Bivariate shrinkage with localvariance estimation [J].IEEE Signal Processing Letters.2002,9(12):438-441[10]Stein C. Estimation of the mean of a multivariate normaldistribution [J].Annuals of Statistics.1981, 9(6):1135-1151
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