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《Optics and Precision Engineering》 2009-05
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Local adaptive image denoising based on double-density dual-tree complex wavelet transform

GONG Wei-guo,LIU Xiao-ying,LI Wei-hong,LI Jian-fu(Key Laboratory for Optoelectronic Technology and System of the Ministry of Education, Chongqing University,Chongqing 400030,China)  
In order to improve the quality of the degraded images,an efficient local adaptive image denoising algorithm based on the Double-density Dual-tree Complex Wavelet Transform(DD-DT CWT) is proposed.The principles and characteristics of the DD-DT CWT are analyzed and a Bivariate Shrinkage Function(BSF) is derivated.Then,the noise image decomposition by the DD-DT CWT is implemented by applying four 2-D Double-density Discrete Wavelet Transform(DD DWT) in parallel and distinct filter sets in the rows and columns.According to the statistical properties of wavelet coefficients and the dependency of inter-level with intra-level coefficients,the BSF with local variance estimation is adopted to process wavelet coefficients and to reconstruct the denoised images by the shrunk wavelet coefficients.Finally,the proposed algorithm is tested on some gray and color noisy images.The experimental results indicate that,compared with the noise images,the Peak Signal-to-Noise Ratio(PSNR) gain of the proposed algorithm has reached 11.72 dB,Mean Structural Similarity(MSSIM) has been 2.7 times higher than that of noise images and the Composite Peak Signal-to-noise Ratio(CPSNR) reaches 11.68 dB when the noise variance is 30.Meanwhile,the algorithm is more efficient in noise removal and edge reservation for all the noise images with different noise variances,which improves the visual quality of the denoised images.
【Fund】: 国家863高技术研究发展计划资助项目(No.2007AA01Z423);; 国防“十一五”基础研究资助项目(No.C10020060355);; 重庆市科技攻关重点资助项目(No.CSTC2007AC2018);; 重庆市自然科学基金资助项目(No.CSTC2008BB2199)
【CateGory Index】: TP391.41
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