抗混叠Curvelet变换非高斯双变量模型图像降噪

闫河; 潘英俊; 刘加伶; 赵明富

doi:null

您当前的位置：

首页 >

文章列表页 >

抗混叠Curvelet变换非高斯双变量模型图像降噪

信息科学 | 更新时间：2020-08-12

- 抗混叠Curvelet变换非高斯双变量模型图像降噪
- Image denoising using non-Gaussian bivariate model based on non-aliasing Curvelet transform
- 光学精密工程 2009年17卷第7期页码：1774-1781
- 作者机构：
  
  1. 重庆大学光电技术及系统教育部重点实验室重庆,400044
  2. 重庆理工大学计算机学院重庆,400054
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No. 50876120)();重庆市科委自然科学基金资助项目( No. CSTC,No.2008BB2340)();重庆市教委科学技术研究项
- DOI：
  中图分类号： TP391.4
- 收稿日期：2008-10-14，
  
  修回日期：2008-11-05，
  
  网络出版日期：2009-07-25，
  
  纸质出版日期：2009-07-25
- 稿件说明：
移动端阅览
闫河, 潘英俊, 刘加伶, 赵明富. 抗混叠Curvelet变换非高斯双变量模型图像降噪[J]. 光学精密工程, 2009,17(7): 1774-1781

YAN He, PAN Ying-jun, LIU Jia-ling, ZHAO Ming-fu. Image denoising using non-Gaussian bivariate model based on non-aliasing Curvelet transform[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1774-1781
闫河, 潘英俊, 刘加伶, 赵明富. 抗混叠Curvelet变换非高斯双变量模型图像降噪[J]. 光学精密工程, 2009,17(7): 1774-1781 DOI：

YAN He, PAN Ying-jun, LIU Jia-ling, ZHAO Ming-fu. Image denoising using non-Gaussian bivariate model based on non-aliasing Curvelet transform[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1774-1781 DOI：

摘要

提出了一种基于非高斯双变量模型复数Curvelet变换的图像降噪新方法。采用具有近似移不变性的复数小波变换代替原Curvelet变换中的小波变换

并用改进的Radon变换避免了原Radon变换中一维傅里叶反变换在频域中采样不足的缺陷

从而保证了新的复数Curvelet变换具有抗混叠性能。充分利用信号系数层间相关性强而噪声系数层间相关性弱的特点

采用非高斯双变量对复数Curvelet变换域系数进行建模

并通过Bayesian MAP估计器对信号系数进行估计

从而实现降噪目的。实验结果表明

本文去噪法得到的峰值信噪比(PSNR)分别比传统Curvelet去噪法和Curvelet域HMT去噪法平均提高2.9 dB和1.5 dB

且能避免重构图像中出现"划痕"和"嵌入污点"

在有效去噪的同时

可较好地保护图像边缘和细节。

Abstract

A new image denoising method using a non-Gaussian bivariate model in a Complex Curvelet Transform(CCT) domain is presented.For avoiding the shift-variance and under-sampling during the 1D inverse Fourier transform in the traditional Curvelet transform

a new Curvelet transform

Complex Curvelet Transform(CCT)

is proposed by adopting the complex wavelet transform and reformative Radon transform to replace the traditional wavelet transform and the old Radon transform respectively

which provides a non-aliasing property for the proposed method. Because the inter-scale correlation of a signal coefficient is stronger than those of noise coefficients

the non-Gaussian bivariate model is used for capturing inter-scale correlation of the signal coefficient and for obtaining the denoised coefficient from the noisy image decomposition by a Bayesian MAP estimator.Experimental results show that the Peak Signel Noise Rotio(PSNR) of the proposed algorithm is averagely higher about 2.9 dB and 1.5 dB than those of the traditional Curvelet transform denoising method and Curvelet domain HMT denoising method respectively at all noise levels.The proposed method avoids "scratching" and "embedded blemishes" phenomena in the reconstructed image

and achieves an excellent balance between suppressing noises effectively and preserving image details and edges as many as possible.

关键词

Keywords

references

CANDES E J, DONOHO D L.Curvelet a surprisingly effective nonadaptive representation for object with edges .C.Rabut A. Cohen, L. L.Schumaker. Curves and Surfaces. Nashville,TN:Vanderbilt University Press,2000:105-120.[2] STACK J L,CANDES E J, DONOHO D L.The Curvelet transform for image denoising[J].IEEE Trans.on Image Processing,2002,11(6):670-684.[3] CANDES E J,DEMANET L,DONOHO D L,et al..Fast discrete Curvelet transforms[J]. Applied and Computational Mathematics, California Institute of Technology, 2005:1-43.[4] 张强,郭宝龙. 应用第二代Curvelet变换的遥感图像融合[J]. 光学精密工程,2007,15(7):1130-1136. ZHANG Q,GUO B L.Fusion of remote sensing images based on second generation Curvelet transform[J].Opt. Precision Eng., 2007,15(7):1130-1136.(in Chinese)[5] 焦李成,谭山. 图像的多尺度几何分析:回顾和展望[J]. 电子学报,2003,31(12A):1975-1981. JIAO L CH,TAN SH.Development and prospect of image multiscale geometric analysis[J].ACTA.Electronic Sinca.,2003,31(12A):1975-1981.(in Chinese)[6] 隆刚,肖磊,陈学佺. Curvelet 变换在图像处理中的应用综述[J]. 计算机研究与发展,2005,42(8):1331-1337. LONG G,XIAO L,CHEN X Q.Overview of the applications of Curvelet transform in image processing [J]. Journal of Computer Research and Development,2005,42(8):1331-1337.(in Chinese)[7] 丁宁,周新志. 基于改进多孔算法的时间序列预测[J]. 系统仿真学报,2007,19(17):4082-4085. DING N,ZHOU X ZH. Time series forecasting based on improved a trous algorithm[J]. Journal of System Simulation,2007,19(17):4082-4085.(in Chinese)[8] 肖小奎,黎绍发. 加强边缘保护的Curvelet图像去噪[J]. 通信学报,2004,25(2):9-15. XIAO X K,LI SH F.Edge-preserving image denoising method using Curvelet transform[J].Journal of China Institute of Communications,2004,25(2):9-15.(in Chinese)[9] KINGSBURY N G. The dual-tree complex wavelet transform:a new technique for shift invariance and directional filters .IEEE Signal Processing Scoiety eds. Proceedings of the 8 th IEEE Digital Signal Processing Workshop,Bryce Canyon UT,USA,1998.[10] SENDUR L,SELESNICK I W.Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J]. IEEE Trans. Signal Processing, 2002,50(11):2744-2756.[11] CHANG S G,BIN Y,VETTERLI M.Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Trans. on Image Processing,2000,9(9):1532-1546.[12] 金海燕,焦李成,刘芳. 基于Curvelet域隐马尔可夫树模型的SAR图像去噪[J]. 计算机学报, 2007,30(3):491-497. JIN H Y,JIAO L CH, LIU F. SAR image denoising based on Curvelet domain Hidden Markov tree models[J]. Chinese Journal of Computer,2007,30(3):491-497.(in Chinese)[13] 李新忠,岱钦,王希军,等. 多尺度小波降噪的数字散斑相关搜索[J]. 光学精密工程,2007,15(1):57-62. LI X ZH,DAI Q,WANG X J,et al..Digital speckle correlation method of multi-scale wavelet noise reduction[J]. Opt. Precision Eng., 2007,15(1):57-62.(in Chinese)[14] 耿则勋,王振国. 改进的天文斑点图像高清晰重建方法[J]. 光学精密工程,2007,15(7):1151-1156. GENG Z X,WANG ZH G.Modified high definition reconstruction algorithm of astronomical speckle images[J]. Opt. Precision Eng.,2007,15(7):1151-1156.(in Chinese)[15] 王明佳,张旭光,韩广良,等. 自适应权值滤波消除图像椒盐噪声的方法[J]. 光学精密工程,2007,15(5):779-783. WANG M J,ZHANG X G,HAN G L,et al.. Elimination of impulse noise by auto-adapted weight filter[J]. Opt. Precision Eng., 2007,15(5):779-783.(in Chinese)

浏览量

393

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

结合复方向滤波器组高斯尺度混合模型及非局部均值滤波的图像去噪

基于非下采样Contourlet变换系数直方图匹配的自适应图像增强

基于图像纹理连续性的4f系统图像NSCT域去噪*

基于双密度双树复小波变换的局域自适应图像去噪