Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (11): 3168-3175.DOI: 10.11772/j.issn.1001-9081.2017.11.3168

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Iterative adaptive weighted-mean filter for image denoising

ZHANG Xinming1,2, CHENG Jinfeng1, KANG Qiang1, WANG Xia1   

  1. 1. College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China;
    2. Engineering Technology Research Center for Computing Intelligence and Data Mining of Henan Province, Xinxiang Henan 453007, China
  • Received:2017-05-16 Revised:2017-06-02 Online:2017-11-10 Published:2017-11-11
  • Supported by:
    This work is partially supported by Key Scientific and Technological Project of Henan Province (132102110209), the Research Program of Basic and Advanced Technology of Henan Province (142300410295).

迭代自适应权重均值滤波的图像去噪

张新明1,2, 程金凤1, 康强1, 王霞1   

  1. 1. 河南师范大学 计算机与信息工程学院, 河南 新乡 453007;
    2. 河南省高校计算智能与数据挖掘工程技术研究中心, 河南 新乡 453007
  • 通讯作者: 张新明
  • 作者简介:张新明(1963-),男,湖北孝感人,教授,硕士,CCF会员,主要研究方向:智能优化算法、数字图像处理、模式识别;程金凤(1990-),女,河南夏邑人,硕士研究生,主要研究方向:数字图像处理;康强(1989-),男,河南郑州人,硕士研究生,主要研究方向:智能优化算法、数字图像处理;王霞(1993-),河南新乡人,硕士研究生,主要研究方向:智能优化算法、数字图像处理。
  • 基金资助:
    河南省重点科技攻关项目(132102110209);河南省基础与前沿技术研究计划项目(142300410295)。

Abstract: Aiming at the deficiencies of the current filters in removing salt-and-pepper noise from images, such as low denoising performance and slow running speed, an image denosing method based on Iterative Adaptive Weighted-mean Filter (IAWF) was proposed. Firstly, a new method was used to construct the neighborhood weight by using the similarity between the neighborhood pixels and the processed point. Then a new weighted-mean filter algorithm was formed by combing the neighborhood weight with switching trimmed mean filter, making full use of the correlation of the image pixels and the advantages of switching trimmed filter, effectively improving the denoising effect. At the same time, the window size of the filter was automatically adjusted to protect the details as much as possible. Finally, the iterative filter was applied to continue until the noisy points were processed completely in order to process automatically and reduce manual intervention. The simulation results show that compared with several state-of-the-art denoising algorithms, the proposed algorithm is better in Peak Signal-to-Noise Ratio (PSNR), collateral distortion and subjective denoising effect under various noise densities, with higher denoising speed, more suitable for practical applications.

Key words: image denoising, weighted-mean filter, iterative filter, adaptive filter, salt-and-pepper noise

摘要: 针对现有滤波方法滤除图像椒盐噪声的性能不理想和耗时长等缺陷,提出了一种迭代自适应权重均值滤波的图像去噪方法(IAWF)。首先,利用图像邻域像素与处理点的相似性采用新型方法构建邻域权重;然后,将此邻域权重与开关裁剪均值滤波结合形成新型权重均值滤波方法,充分利用像素间的相关性和开关裁剪滤波的优势,有效提高了算法的去噪效果,同时采用自适应的方式调整滤波窗口大小,以便尽可能地保护图像细节;最后,采用迭代式滤波方法,即如果上述操作还没有处理完噪声点,则迭代去噪直至噪声点处理完毕,实现自动处理。仿真实验结果表明,在各种不同噪声密度下,IAWF在峰值信噪比(PSNR)、失真度,以及视觉效果等方面均优于现有的几种优秀的滤波算法,且具有更快的运行速度,更适用于实际应用场合。

关键词: 图像去噪, 权重均值滤波, 迭代滤波, 自适应滤波, 椒盐噪声

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