Non-local means denoising algorithm based on image segmentation

doi:10.11772/j.issn.1001-9081.2017.07.2078

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 2078-2083.DOI: 10.11772/j.issn.1001-9081.2017.07.2078

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Non-local means denoising algorithm based on image segmentation

XU Su^1,2, ZHOU Yingyue^1,2

1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China;
2. State Key Laboratory of Robot Technology Used for Special Environment(Southwest University of Science and Technology), Mianyang Sichuan 621010, China

Received:2016-12-19 Revised:2017-03-08 Online:2017-07-18 Published:2017-07-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61401379), the Doctoral Fund of Southwest University of Science and Technology (11zx7134).

基于图像分割的非局部均值去噪算法

徐苏^1,2, 周颖玥^1,2

1. 西南科技大学信息工程学院, 四川绵阳 621010;
2. 特殊环境机器人技术四川省重点实验室(西南科技大学), 四川绵阳 621010

通讯作者: 徐苏
作者简介:徐苏(1983-),女,四川绵阳人,讲师,博士,主要研究方向:图像处理与分析、模式识别、深度学习;周颖玥(1983-),女,四川马尔康人,副研究员,博士,主要研究方向:图像处理与分析、深度学习。
基金资助:
国家自然科学基金资助项目（61401379）；西南科技大学博士基金资助项目（11zx7134）。

Abstract

Abstract: Focusing on the problems of non-adaption of filtering parameters and edge blur of Non-Local Means (NLM) algorithm, an improved NLM denoising algorithm based on image segmentation was proposed. The proposed algorithm is composed of two phases. In the first phase, the filtering parameter was determined according to the noise level and image structure, and traditional NLM algorithm was used to remove the noise and generate the rough clean image. In the second phase, the estimated clean image was divided into detailed region and background region based on pixel variance, and the image patches belonged to different regions were denoised separately. To effectively remove the noise, the back projection was utilized to make full use of the residual structure from the method noise of the first phase. The experimental results show that compared with traditional NLM and three NLM-improved algorithms, the proposed algorithm achieves higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM), while maintaining more structure details and edges, making the denoised image clear and retaining the complete real information.

Key words: image denoising, Non-Local Mean (NLM), image segmentation, noise standard deviation, similarity measurement

摘要： 针对传统非局部均值（NLM）算法的滤波参数非自适应及去噪后边缘易模糊的缺点，提出一种基于图像分割的非局部均值去噪算法。该算法分为两个阶段：第一阶段根据噪声大小及图像纹理自适应确定滤波参数的值，并采用传统非局部均值算法得到去噪结果图；第二阶段根据像素点方差的不同，将该去噪结果图分为细节区域和背景区域，再对属于不同区域的图像块分别去噪，同时为了更有效地去除噪声，还采用了反向投影的方式，充分利用了第一阶段方法噪声中残留的结构信息。实验结果表明，与传统非局部均值算法及其三种改进算法相比，所提算法的峰值信噪比（PSNR）及结构相似性（SSIM）更高，纹理细节和边缘结构更完整，图像更清晰，本真信息保留更完整。

关键词: 图像去噪, 非局部均值, 图像分割, 噪声标准差, 相似性度量

CLC Number:

TP391.413

XU Su, ZHOU Yingyue. Non-local means denoising algorithm based on image segmentation[J]. Journal of Computer Applications, 2017, 37(7): 2078-2083.

徐苏, 周颖玥. 基于图像分割的非局部均值去噪算法[J]. 计算机应用, 2017, 37(7): 2078-2083.

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Non-local means denoising algorithm based on image segmentation

基于图像分割的非局部均值去噪算法

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