Image denoising with dual-directional filter bank GSM model and non-local mean filter

LIU Qiao-hong; LI Bin; LIN Min

doi:10.3788/OPE.20142210.2806

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Image denoising with dual-directional filter bank GSM model and non-local mean filter

更新时间：2020-08-13

- Image denoising with dual-directional filter bank GSM model and non-local mean filter
- Optics and Precision Engineering Vol. 22, Issue 10, Pages: 2806-2814(2014)
- 作者机构：
  
  上海大学机电工程与自动化学院上海,200072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142210.2806
  CLC： TP391.4
- Received：20 March 2014，
  
  Revised：29 April 2014，
  
  Published：25 October 2014
- 稿件说明：
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刘巧红, 李斌, 林敏. 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[J]. 光学精密工程, 2014,22(10): 2806-2814

LIU Qiao-hong, LI Bin, LIN Min. Image denoising with dual-directional filter bank GSM model and non-local mean filter[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2806-2814
刘巧红, 李斌, 林敏. 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[J]. 光学精密工程, 2014,22(10): 2806-2814 DOI： 10.3788/OPE.20142210.2806.

LIU Qiao-hong, LI Bin, LIN Min. Image denoising with dual-directional filter bank GSM model and non-local mean filter[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2806-2814 DOI： 10.3788/OPE.20142210.2806.

摘要

提出了一种结合金字塔对偶树方向滤波器组(PDTDFB)变换域高斯尺度混合模型及非局部均值滤波的图像去噪方法.首先

建立了含噪图像的PDTDFB系数的局部高斯尺度混合模型

应用贝叶斯最小二乘法估计出去噪图像的PDTDFB系数;然后

通过PDTDFB逆变换重构得到初步去噪的图像;最后

采用非局部均值滤波平滑人工效应

从而获取最终的去噪图像.该方法充分利用了PDTDFB变换具有近似平移不变性、多尺度多方向选择性和对图像纹理边缘等细节信息的高效表示能力

以及高斯尺度混合模型对PDTDFB系数的邻域相关性的概括能力.实验结果表明:与目前几个典型的去噪方法相比较

该方法使信噪比提高了0.3~3 dB

视觉效果也有明显的改善.另外

该方法不仅能有效地去除含噪图像中的噪声

同时也有效地保留了原始图像中的边缘和纹理等细节信息.

Abstract

A new image denoising method combined a Pyramidal Dual-tree Complex Directional Filter Bank(PDTDFB) domain Gaussian Scale Mixture(GSM) model and a non-local mean filter was proposed. First

the locally coefficients PDTDFB(GSM) model for a noisy image was established

and the denoised coefficients were estimated by the Bayes least square estimator. Then

the inverse PDTDFB transform was used to obtain the preliminary denoised image. Finally

Nonlocal Mean Filter(NLMF) was employed to smooth the artifacts of the preliminary denoised image and to obtain the final denoised image. This method combines the characters of the PDTDFB on shift-invariance

multi-directional selectivity

image edge representation and the effective ability of GSM model for capturing correlation of neighbor coefficients. Experimental results indicate that the proposed method has removed Gaussian white noise while effectively preserving edges and texture information. Comparing with some outstanding denoised methods

its Peak Signal to Noise Radio(PSNR) value increases 0.3-3 dB and visual quality is obviously improved.

关键词

Keywords

references

DONOHO D L.De-noising by soft-thresholding[J].IEEE Trans. on Inform. Theory,1995,41(3):613-627.

CHANG S G, YU B, VETTERLI M. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Processing, 2000, 9(9):1532-1546.

SENDUR L,SELESNICK I W. Bivariate shrinkage functions forwavelet-based exploiting interscale dependency [J]. IEEE Trans. Signal Process., 2002, 50(11):2744-2756.

LUISIER F, BLU T, UNSER M. A new SURE approach to image denoising: interscale orthonormal wavelet thresholding[J]. IEEE Trans. Image Process,2007,16(3):593-606.

PORTILLA J, STRELA V, WAINWRIGHT M J,et al.. Image denoising using scale mixtures of Gaussians in the wavelet domain[J]. IEEE Trans. Image Process,2003,12(11):1338-1351.

PIZURICA A, PHILIPS W. Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising[J]. IEEE Trans. Image Process.,2006,15(3):645-665.

龚俊亮,何昕,魏仲慧,等. 基于贝叶斯估计的剪切波域局部自适应图像去噪[J]. 液晶与显示,2013,28(5):799-804. GONG J L,HE X,WEI ZH H,et al..Local adaptive image denoising based on Bayesian estimation in shearlet domain[J].Chinese Journal of Liquid Crystals and Displays,2013,28(5),799-804. (in Chinese)

唐艳秋,张星祥,李新娥,等. 基于人眼视觉灰度识别特性的图像动态范围小波变换处理方法[J]. 液晶与显示,2012,27(3):385-390. TANG Y Q,ZHANG X X,LI X E,et al..Image processing method of dynamic range with wavelet transform based on human visual gray recognition characteristics[J]. Chinese Journal of Liquid Crystals and Displays,2012,27(3),385-390. (in Chinese)

傅瑶,孙雪晨,薛旭成,等. 基于非下采样轮廓波变换的全色图像与多光谱图像融合方法研究[J]. 液晶与显示,2013,28(3),429-434. FU Y,SUN X CH,XUE X CH,et al..Panchromatic and multispectral image fusion method based on nonsubsampled Contourlet transform[J]. Chinese Journal of Liquid Crystals and Displays,2013,28(3),429-434. (in Chinese)

CANDES E. Ridgelets: Theory and Applications, Department of Statistics[M]. US Stanford University, 1998.

STARCK J L,CANDES E J,DONOHO D L. The curvelet transform for image denoising[J].IEEE Trans. Image Process., 2002,21:131-141.

DO M N,VETTERLI M.The Contourlet transform: an efficient directional multiresolution image representation[J].IEEE Trans. Image Process., 2005,14:2091-2106.

NGUYEN T T, ORAINTARA S. The shiftable complex directional pyramid: Part I. Theoretical aspects [J]. IEEE Trans. Signal Process.,2008,56(10):4651-4660.

高国荣,许录平,冯冬竹. 利用非抽样Shearlet域GSM模型进行图像去噪[J]. 武汉大学学报:信息科学版,2013,28(7),778-782. GAO G R,XU L P,FENG D ZH.Image denoising based on the NSST domain GSM model[J].Geomatics and Information Science of Wuhan University,2013,28(7),778-782. (in Chinese)

刘帅奇,胡绍海,肖扬. 基于复Shearlet域高斯混合模型的SAR图像去噪[J]. 航空学报,2013,34(1),173-180. LIU SH Q,HU SH H,X Y.SAR image denosing based on complex Shearlet transform domain Gaussian mixture model[J].Acta Aeronautica et Astronautica Sinica, 2013,34(1),173-180. (in Chinese)

LIU J P. Spatial-temporal fusion for flotation froth image denoising based on BLS-GSM method in curvelet domain[J]. IEEE Transactions on Electrical and Electronic Engineering,2014,9(1):31-38.

闫河,余永辉,赵明富. 基于移不变抗混叠轮廓波变换的混合统计模型图像降噪[J]. 光学精密工程,2010,18(10),2269-2279. YAN H,YU Y H, ZHAO M F.Mixed statistical model image denoising based on shift-invariant non-aliasing Contourlet transfrom [J]. Opt. Precision Eng., 2010,18(10),2269-2279. (in Chinese)

BUADES A,COLL B,MOREL J M. A non-local algorithm for image denoising[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR,2005: 60-65.

任文琦,王元全. 基于梯度矢量卷积场的四阶各向异性扩散及图像去噪[J]. 光学精密工程,2013,21(10),2714-2719. REN W Q,WANG Y Q.GVC-based fourth-order anisotropic diffusion for image denosing[J]. Opt. Precision Eng.,2013,21(10),2714-2719. (in Chinese)

李家德,张叶,贾平. 采用非局部均值的超分辨率重构[J]. 光学精密工程,2013,21(6),1576-1585. LI J D,ZHANG Y,JIA P. Super-resolution reconstruction using nonlocal means[J].Opt. Precision Eng.,2013,21(6),1576-1585. (in Chinese)

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