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

刘巧红; 李斌; 林敏

doi:10.3788/OPE.20142210.2806

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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

信息科学 | 更新时间：2020-08-13

- 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
- Image denoising with dual-directional filter bank GSM model and non-local mean filter
- 光学精密工程 2014年22卷第10期页码：2806-2814
- 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：
  
  上海市教育委员会科研创新资金资助项目(No.14YZ169)()
- DOI：10.3788/OPE.20142210.2806
  中图分类号： TP391.4
- 收稿日期：2014-03-20，
  
  修回日期：2014-04-29，
  
  纸质出版日期：2014-10-25
- 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

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