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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

28

期数:

2018年05期

页码:

9-12

栏目:

智能、算法、系统工程

出版日期:

2018-05-10

文章信息/Info

Title:

A Periodic Noise Elimination Algorithm Based on Morphological Filtering with Auto－adapted Weights

文章编号:

1673-629X(2018)05-0009-04

作者:

戴丹1 ; 张兴刚 2

1.贵州大学 计算机科学与技术学院,贵州 贵阳 550025;
2.贵州大学 物理学院,贵州 贵阳 550025

Author(s):

DAI Dan1 ; ZHANG Xing-gang2

1.School of Computer Science and Information,Guizhou University,Guiyang 550025,China;
2.Institute of Physics,Guizhou University,Guiyang 550025,China

关键词:

周期性噪声; 图像去噪; 自适应权重; 形态滤波

Keywords:

periodic noise; image denoising; auto－adapted weights; morphological filter

分类号:

TN911.73;TP391

DOI:

10.3969/j.issn.1673-629X.2018.05.003

文献标志码:

A

摘要:

针对去除周期性噪声的同时容易造成图像的失真或降噪效果不理想的问题，提出了一种基于权重自适应形态学的周期性噪声去除方法。该方法使用不同尺度的结构元素对图像的周期性噪声进行串行处理，再将串行处理的结果并行处理，并通过自适应权值算法来构建复合级联滤波器，使用该滤波器滤除图像的周期性噪声。为了验证该算法的去噪性能，对周期性噪声及混合噪声进行了常用去噪算法的对比性实验。结果表明，视觉上，使用该算法去噪后的图像去噪效果较好且图像边缘和细节比较清晰;定量评价标准上，使用该算法去噪后的图像的 PSNＲ 和 SSIM 都较高。因此，该算法有效地抑制了图像中的周期性噪声，同时较好地保持了图像的几何特征，具有更好的鲁棒性。

Abstract:

Aiming at the problem that it is easy to cause the image distortion or poor noise reduction while eliminating the period noise,we propose a periodic denoising method based on morphological filtering with auto－adapted weights．In this method,periodic noise of images is processed serially by structural elements of different scales,then the results of serial processing are processed in parallel by constructing composite cascade filter using multi－structural elements.In order to verify the denoising performance of the proposed algorithm,some denoising algorithms are used to eliminate periodic noise and mixed noise.The experiments show that the de－noised image obtained by the proposed algorithm is less residual noise and clearer textures than other algorithms visually．At the same time,in the quantitative evaluation standard,the PSNR and SSIM of de－noised image obtained by the proposed algorithm are higher.So,it is robust,not only effectively restraining periodic and mixed noise,but also preferably maintaining image geometry characteristic.

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更新日期/Last Update: 2018-06-26