光子学报 ›› 2019, Vol. 48 ›› Issue (9): 910003-0910003.doi: 10.3788/gzxb20194809.0910003

• 图像处理 • 上一篇    下一篇

基于高斯曲率优化和非下采样剪切波变换的高密度混合噪声去除算法

王满利1,2, 田子建1, 桂伟峰2, 吴君2   

  1. 1. 中国矿业大学(北京) 机电与信息工程学院, 北京 100083;
    2. 河南理工大学 物理与电子信息学院, 河南 焦作 454000
  • 收稿日期:2019-04-30 出版日期:2019-09-25 发布日期:2019-07-09
  • 通讯作者: 田子建(1964-),男,教授,博导,主要研究方向为信息与通信技术.Email:Tianzj0726@126.com
  • 作者简介:王满利(1981-),男,博士研究生,主要研究方向为信息与通信技术.Email:wml920@163.com
  • 基金资助:

    国家自然科学基金(No.51674269)

High Density Mixed Noise Removal Algorithm Based on Gaussian Curvature Optimization and Non-subsampled Shearlet Transform

WANG Man-li1,2, TIAN Zi-jian1, GUI Wei-feng2, WU Jun2   

  1. 1. School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing, Beijing 100083, China;
    2. School of Physics & Electronic Information Engineering, HeNan Polytechnic University, Jiaozuo, Henan 454000, China
  • Received:2019-04-30 Online:2019-09-25 Published:2019-07-09
  • Supported by:

    National Natural Science Foundation of China (No. 51674269)

摘要:

为提高矿井混合噪声图像的可观测性,提出了基于高斯曲率优化和非下采样剪切波变换的高密度混合噪声去除算法.使用局部高斯曲率优化混合噪声图像,抑制椒盐噪声对噪声分布的影响,使混合噪声分布近似为高斯噪声分布.使用非下采样剪切波变换分解高斯曲率优化图像,实施自适应硬阈值收缩降噪,去除混合噪声中的高斯噪声成分.最后,迭代使用局部高斯曲率优化和非下采样剪切波变换降噪去除残余噪声,直至输出图像梯度能量满足停止条件.实验表明,本文算法能够有效地去除高斯噪声和椒盐噪声构成的高密度混合噪声,且有效抑制了剪切波变换降噪引起的伪吉布斯现象,有效地降低了矿井图像的噪声.

关键词: 高斯曲率优化, 非下采样剪切波变换, 混合噪声, 阈值收缩, 图像降噪

Abstract:

In order to improve the observability of mine images corrupted by mixed noise, a high-density mixed noise removal algorithm based on Gaussian curvature optimization and non-subsampled shearlet transform was proposed. The local Gaussian curvature is used to optimize the mixed noise image to suppress the influence of salt & pepper noise on the noise distribution, which makes the mixed noise distribution approximate to a Gaussian noise distribution. Then, the non-subsampled shearlet transform is used to decompose the image optimized by Gaussian curvature and implement adaptive hard threshold shrinkage to remove the Gaussian noise in the mixed noise. Finally, the local Gaussian curvature optimization and the non-subsampled shearlet transform are executed iteratively to reduce the residual noise until the output image gradient energy satisfies the stop condition. Experiments show that the proposed algorithm can effectively remove the high-density mixed noise composed of Gaussian noise and salt and pepper noise, and effectively suppress the Pseudo-Gibbs phenomenon caused by shearlet transform denoising algorithms, and effectively reduce the noise of mine images.

Key words: Gaussian curvature optimization, Mixed noise, Threshold shrinkage, Image denoising, Non-subsampled shearlet transform

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