Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain

GONG Jun-liang; HE Xin; WEI Zhong-hui; WANG Fang-yu

doi:10.3788/YJYXS20132805.0799

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Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain
- Chinese Journal of Liquid Crystals and Displays Vol. 28, Issue 5, Pages: 799-804(2013)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20132805.0799
  CLC： TP391
- Received：05 May 2012，
  
  Revised：07 June 2012，
  
  Published：15 October 2013
- 稿件说明：
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龚俊亮, 何昕, 魏仲慧, 王方雨. 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 Jun-liang, HE Xin, WEI Zhong-hui, WANG Fang-yu. Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain[J]. , 2013,28(5): 799-804
龚俊亮, 何昕, 魏仲慧, 王方雨. 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 DOI： 10.3788/YJYXS20132805.0799.

GONG Jun-liang, HE Xin, WEI Zhong-hui, WANG Fang-yu. Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain[J]. , 2013,28(5): 799-804 DOI： 10.3788/YJYXS20132805.0799.

摘要

提出一种基于贝叶斯估计和剪切波变换相结合的图像去噪算法。对含有加性高斯白噪声的图像进行剪切波变换

得到各尺度各方向上的剪切波系数;利用剪切波系数的相关性

以当前子带剪切波系数为中心

选取尺寸合适的邻域窗口

以该窗口为单位

在贝叶斯最大后验概率准则下推导出基于剪切波系数为拉普拉斯先验分布的最大后验估计表达式和子带阈值

再通过软化处理达到系数收缩的目的;对处理后的剪切波系数反变换

得到处理后的图像。实验表明

与传统的小波域去噪算法相比

该方法获得了明显的峰值信噪比增益

主观视觉效果也得到了改善。

Abstract

Based on the combination of Bayesian estimation and Shearlet transform

an algorithm for image denoising is proposed. In order to get Shearlet coefficients in all scales and directions

image with additive white Gaussian noise is processed by Shearlet transform. With the dependencies of the Shearlet coefficients

the new method select a proper neighbo-ring window by centering the current coefficient within it

and conclude the MAP expression and sub-band thresholds under Bayesian maximum posterior probability criteria when Shearlet coefficients is thought to have Laplace prior distribution

then made shrinkage on it by soft threshold. Inverse Shearlet transform is performed to the processed coefficients and get the denoised image. The experimental results demonstrate that compared with traditional wavelet domain denoising algorithms

the proposed method not only improves peak ratio of signal to noise but also have a remarkable visual effects.

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references

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