计算机应用 ›› 2021, Vol. 41 ›› Issue (8): 2312-2317.DOI: 10.11772/j.issn.1001-9081.2020101684

所属专题: 多媒体计算与计算机仿真

• 多媒体计算与计算机仿真 • 上一篇    下一篇

基于人工蜂群与模糊C均值的自适应小波变换的噪声图像分割

石雪松1, 李宪华1, 孙青2, 宋韬2   

  1. 1. 安徽理工大学 机械工程学院, 安徽 淮南 232001;
    2. 上海大学 机电工程与自动化学院, 上海 200444
  • 收稿日期:2020-10-30 修回日期:2021-01-04 出版日期:2021-08-10 发布日期:2021-01-27
  • 通讯作者: 李宪华
  • 作者简介:石雪松(1994-),男,安徽安庆人,硕士研究生,主要研究方向:机器视觉、移动机器人视觉SLAM;李宪华(1980-),男,山东济南人,教授,博士,主要研究方向:机器人技术;孙青(1993-),男,安徽合肥人,博士研究生,主要研究方向:机器人技术;宋韬(1983-),男,辽宁大连人,博士,主要研究方向:机器人技术。
  • 基金资助:
    国家自然科学基金资助项目(61803251);安徽高校自然科学研究重点资助项目(KJ2016A200);安徽省科技重大专项(16030901012);上海市机器人研发与转化功能型平台开放课题(K2020468);安徽理工大学研究生创新基金资助项目(2019CX2037)。

Noise image segmentation by adaptive wavelet transform based on artificial bee swarm and fuzzy C-means

SHI Xuesong1, LI Xianhua1, SUN Qing2, SONG Tao2   

  1. 1. School of Mechanical Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China;
    2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
  • Received:2020-10-30 Revised:2021-01-04 Online:2021-08-10 Published:2021-01-27
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61803251), the Anhui University Natural Science Research Key Funding Project (KJ2016A200), the Anhui Science and Technology Major Project (16030901012), the Opening Project of Shanghai Robot Research and Development and Transformation Functional Platform (K2020468), the Postgraduate Innovation Fund of Anhui University of Science and Technology (2019CX2037).

摘要: 针对传统模糊C均值(FCM)聚类算法在处理噪声图像时易受到噪声影响的问题,提出了基于FCM的小波域特征增强的噪声图像分割方法。首先,将噪声图像进行二维小波分解;其次,对近似系数进行边缘增强,同时利用人工蜂群(ABC)优化算法对细节系数进行阈值处理,并将处理后的系数进行小波重构;最后,对重构后的图片使用FCM算法来进行图像分割。选取5幅典型的灰度图像,分别添加高斯噪声和椒盐噪声,使用多种方法进行分割,以分割后图像的峰值信噪比(PSNR)和误分率(ME)作为性能指标,实验结果表明,所提方法分割后的图片相较于传统FCM聚类算法分割方法和粒子群优化(PSO)分割方法分割后的图片在PSNR上最多分别有281%和54%的提升,在ME上最多分别有55%和41%的降低。可见所提出的分割方法较好地保留了图像边缘纹理信息,其抗噪性能与分割性能得到了提升。

关键词: 模糊C均值, 小波分解, 人工蜂群, 小波重构, 峰值信噪比, 误分率

Abstract: Aiming at the problem that traditional Fuzzy C-Means (FCM) clustering algorithm is easily affected by noise in processing noise images, a noise image segmentation method of wavelet domain feature enhancement based on FCM was proposed. Firstly, the noise image was decomposed by two-dimensional wavelet. Secondly, the approximate coefficient was enhanced at the edge, and Artificial Bee Colony (ABC) optimization algorithm was used to perform threshold processing to the detail coefficients, and then the wavelet reconstruction was carried out for the processed coefficients. Finally, the reconstructed image was segmented by FCM algorithm. Five typical grayscale images were selected, and were added with Gaussian noise and salt-and-pepper noise respectively. Various methods were used to segment them, and the Peak Signal-to-Noise Ratio (PSNR) and Misclassification Error (ME) of the segmented images were taken as performance indicators. Experimental results show that the PSNR of the images segmented by the proposed method is at most 281% and 54% higher than the PSNR of the images segmented by the traditional FCM clustering algorithm segmentation method and Particle Swarm Optimization (PSO) segmentation method respectively, and the segmented images of the proposed method has the ME at most 55% and 41% lower than those of the comparison methods respectively. It can be seen that the proposed segmentation method preserves the edge texture information well, and the anti-noise and segmentation performance of this method are improved.

Key words: Fuzzy C-Means (FCM), wavelet decomposition, Artificial Bee Colony (ABC), wavelet reconstruction, Peak Signal-to-Noise Ratio (PSNR), Misclassification Error (ME)

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