基于加权空间离群点度量的随机脉冲噪声降噪算法

doi:10.11772/j.issn.1001-9081.2016.10.2826

计算机应用 ›› 2016, Vol. 36 ›› Issue (10): 2826-2831.DOI: 10.11772/j.issn.1001-9081.2016.10.2826

基于加权空间离群点度量的随机脉冲噪声降噪算法

杨昊^1,2, 陈雷霆^1,2, 邱航^1,2

1. 电子科技大学计算机科学与工程学院, 成都 611731;
2. 数字媒体技术四川省重点实验室, 成都 611731

收稿日期:2016-03-28 修回日期:2016-06-28 发布日期:2016-10-10
通讯作者: 陈雷霆,E-mail:richardchen@uestc.edu.cn
作者简介:杨昊(1981—),男,四川成都人,博士研究生,CCF会员,主要研究方向:数字图像处理;陈雷霆(1966—),男,四川大竹人,教授,博士生导师,博士,主要研究方向:计算机图形学、数字图像处理;邱航(1978—),男,四川成都人,副教授,博士,主要研究方向:计算机图形学、数字图像处理。
基金资助:
国家自然科学基金资助项目（61202255）；教育部广东省产学研重大专项（2012A090300001）。

Denoising algorithm for random-valued impulse noise based on weighted spatial local outlier measure

YANG Hao^1,2, CHEN Leiting^1,2, QIU Hang^1,2

1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China;
2. Digital Media Technology Key Laboratory of Sichuan Province, Chengdu Sichuan 611731, China

Received:2016-03-28 Revised:2016-06-28 Published:2016-10-10
Supported by:
BackgroundThis work is partially supported by the National Natural Science Foundation of China (61202255), the Special Project on the Integration of Industry, Education and Research of Guangdong Province (2012A090300 001).

摘要/Abstract

摘要： 针对排序统计类降噪算法在随机脉冲噪声（RVIN）图像降噪过程中，对图像边缘和细节部分噪声识别不够准确以及恢复比较模糊的问题，提出了基于加权空间离群点度量（SLOM）的脉冲噪声降噪算法WSLOM-EPR。该算法以优化的空间距离差为基础，引入图像邻域均值和标准差，建立反映局部边缘细节特征的噪声检测方法，提高边缘细节处噪声的识别精度；然后以精确检测结果为基础，优化保边正则（EPR）函数，提高算法的执行效率，并增强算法保留边缘细节的能力。仿真结果显示，WSLOM-EPR算法在40%到60%噪声密度下对噪声点的误检和漏检综合表现优于对比算法，且能在两者之间保持一个较好的平衡；降噪后的峰值信噪比（PSNR）好于对比算法中的大多数情况，且边缘细节在视觉上更加清晰连续。结果表明WSLOM-EPR算法提高了噪声检测精度，有效地保持了恢复图像的边缘细节信息。

关键词: 随机脉冲噪声, 空间离群点度量, 局部统计, 保边正则, 降噪

Abstract: In order to alleviate the problem of inaccurate noise identifying and blurred restoration in image edges and details, a novel algorithm based on weighted Spatial Local Outlier Measure (SLOM) was proposed for removing random-valued impulse noise, namely WSLOM-EPR. Based on optimized spatial distance difference, the mean and standard deviation of neighborhood were introduced to set up a noise detection method for reflecting local characters in image edges, which could improve the precision of noise identification in edges. According to the precision detection results, the Edge-Preserving Regularization (EPR) function was optimized to improve the computation efficiency and preserving capability of edges and details. The simulation results showed that, with 40% to 60% noisy level, the overall performance in noise points detection was better than that of the contrast detection algorithms, which can maintain a good balance in false detection and miss detection of noise. The Peak Signal-to-Noise Ratios (PSNR) of WSLOM-EPR was better than that of the most of the contrast algorithms, and the restoring image had clear and continuous edges. Experimental results show that WSLOM-EPR can improve detection precision and preserve more edges and details information.

Key words: Random-valued Impulse Noise (RVIN), Spatial Local Outlier Measure (SLOM), local statistics, Edge-Preserving Regularization (EPR), denoising

中图分类号:

TP391.41

杨昊, 陈雷霆, 邱航. 基于加权空间离群点度量的随机脉冲噪声降噪算法[J]. 计算机应用, 2016, 36(10): 2826-2831.

YANG Hao, CHEN Leiting, QIU Hang. Denoising algorithm for random-valued impulse noise based on weighted spatial local outlier measure[J]. Journal of Computer Applications, 2016, 36(10): 2826-2831.

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基于加权空间离群点度量的随机脉冲噪声降噪算法

Denoising algorithm for random-valued impulse noise based on weighted spatial local outlier measure

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