基于双树复小波变换的BivaShrink自选窗图像去噪算法
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摘要
在小波变换理论和双变量模型的研究基础上,本文提出了一种BivaShrink自选窗算法,该算法根据邻域内小波系数的相关度大小自适应选取邻域窗口。最后将双树复小波变换应用在BivaShrink自选窗图像去噪算法中。实验结果证明,BivaShrink自选窗优于BivaShrink去噪算法,与传统的离散小波变换相比,双树复小波自选窗图像去噪效果优于BivaShrink自选窗。
Based on wavelet transform and bivariate model,this paper proposed an adaptive select window algorithm of BivaShrink,it adaptive select neighborhood window according to the correlation degree of wavelet coefficients.Finally the DTCWT apply in the algorithm.Experimental tests reveal that adaptive select window algorithm of BivaShrink is better than BivaShrink,compared with traditional discrete wavelet transform,the algorithm apply on DTCWT is better than daptive select window algorithm of BivaShrink.
Based on wavelet transform and bivariate model,this paper proposed an adaptive select window algorithm of BivaShrink,it adaptive select neighborhood window according to the correlation degree of wavelet coefficients.Finally the DTCWT apply in the algorithm.Experimental tests reveal that adaptive select window algorithm of BivaShrink is better than BivaShrink,compared with traditional discrete wavelet transform,the algorithm apply on DTCWT is better than daptive select window algorithm of BivaShrink.
引文
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[2]DONOHO D L,JOHNSTONE I M.Adapting to Unknown Smoothness Via Wavelet Shrinkage[J].American Statical Assoc,1995,90(432):1200-1224.
[3]吕俊白,蔡灿辉.一种有效保留图像细节的自适应图像消噪方法[J].计算机应用,2010,30(8):2077-2079.
[4]PAN Jin-feng,PAN Xue-feng.The Adaptive Bivariate Shrinkage Denoising Method[J].978-0-7695-4225-6/10,IEEE Computer Society,2010.
[5]SENDUR L,SELESNICK W.Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J].IEEE Trans.Image Processing.2002,50(11):2744-2756.
[6]REKABDAR A,KHAYAT O.Using Bivariate Gaussian Distribution for Image Denoising in the 2-D Complex Wavelet Domain[J].978-1-4244-9708-9/10,IEEE,2010.
[7]刘鑫,贺振华,黄德济.基于双变量收缩函数的局域自适应图像去噪[J].计算机应用,2006,26.
[8]董雪燕,郑永果.具有自适应窗口的双变量模型图像去噪方法[J].计算机应用与软件,2012,(6):135-136.
[9]侯建华.基于小波及其统计特性的图像去噪方法研究[D].武汉:华中科技大学,2007.
[10]汤辉.基于小波及其统计模型的图像去噪研究[D].昆明:昆明理工大学,2014.
[11]李江涛,倪国强,王强.基于双树复数小波变换和双变量萎缩阈值图像降噪[J].光学技术,2007,33(5).
[12]张汗灵,熊先越.基于复小波变换与层间模型的图像去噪[J].光电工程,2006,33(11).
[13]KINGSBURY N G.Image Processing with the complex wavelet[D].Phil.Trans Royal Society London A September,1999.
[14]REKABDAR A,KHAYAT O.Using Bivariate Gaussian Distribution for Image Denoising in the 2-D Complex Wavelet Domain[J].978-1-4244-9708-9/10,IEEE,2010.
[15]张稳稳.双树复小波域的邻域自适应贝叶斯收缩去噪[J].计算机工程与应用,2012,48(31).
[16]罗鹏,高协平.基于双树复数小波变换的图像去噪方法[J].光子学报,2008,37(3):604-608.
[17]刘蕾.基于双树复小波变换的图像去噪[D].北京:北京化工大学,2010.
[18]殷明,白瑞峰,邢燕,等.基于非下采样双树复小波域的双变量模型去噪算法[J].光子学报,2014,43(10):131-137.
[19]龚卫国,刘晓营,李伟红,等.双密度双树复小波变换的局域自适应图像去噪[J].光学精密工程,2009,17(5):1171-1180.
[20]高洪玉,裴连群.双树复小波域农业图像改进半软半硬阈值函数去噪[J].江苏农业科学,2015,(9):450-452.