计算机与现代化

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基于饱和系统随机共振的图像去噪算法

  

  1. (青岛大学计算机科学技术学院,山东青岛266071)
  • 收稿日期:2019-05-08 出版日期:2019-12-11 发布日期:2019-12-11
  • 作者简介:殷学杰(1990-),男,山东诸城人,硕士研究生,研究方向:图像处理,E-mail: 897391576@qq.com; 通信作者:马玉梅(1980-),女,山东昌邑人,副教授,博士,研究方向:非线性信号处理,E-mail: mayumei_qdu@163.com; 潘振宽(1966-),男,山东昌邑人,教授,博士生导师,博士,研究方向:计算机视觉与图像处理,E-mail: zkpan@qdu.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(61501276, 61772294); 中国博士后科学基金面上资助项目(2016M592139); 青岛市博士后应用研究项目(2015120)

#br# Image Denoising Algorithm Based on Stochastic Resonance of Saturating System

  1. (College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)
  • Received:2019-05-08 Online:2019-12-11 Published:2019-12-11

摘要: 利用随机共振(SR)机制,在传输相关信号的非线性系统中加入噪声,在输出端信号可被增强。本文提出一种基于动态饱和非线性系统随机共振的图像去噪算法,首先将图像重采样为一维信号,并调节动态饱和非线性系统的参数,使之达到最优,使动态饱和非线性系统能够产生随机共振。相比一维随机共振,二维随机共振的图像复原效果更接近于原图,输出图像的直方图和峰值信噪比(PSNR),也明显优于一维随机共振。相比于传统滤波方法,饱和系统的去噪效果更好,同时对于噪声强度的变化具有较好的鲁棒性。

关键词: 随机共振, 饱和非线性系统, 图像去噪, 灰度图像, 峰值信噪比

Abstract: Stochastic resonance (SR) is a mechanism that noise can be added to the nonlinear system for transmitting the certain signals, and the output can be enhanced. In this paper, we propose an image denoising algorithm based on stochastic resonance of dynamical saturating nonlinear systems. Firstly, the image is resampled into one-dimensional signals, and the parameters of the saturating nonlinear system can be tuned optimally, then the SR effect can be obtained in the saturating nonlinear system. Comparing with the effect of the one-dimensional SR, the effect of image restoration of the two-dimensional SR is closer to the original image and it is showed that the effect of image enhancement of the two-dimensional SR is superior by the output histogram and peak signal-to-noise ratio (PSNR). Compared with traditional filtering methods, the denoising effect of the saturating nonlinear system is better and it is more robust to the change of noise intensity.

Key words: stochastic resonance, saturating nonlinear system, image denoising, grayscale image, peak signal to noise ratio

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