基于改进双边滤波与随机共振的图像去噪算法研究

Research on image denoising algorithm based on improved bilateral filtering and stochastic resonance

随机共振区别于均值滤波、双边滤波等传统信号处理方法,它在非线性系统作用下,将噪声变害为利来处理强噪声背景下的弱信号。在充分利用双边滤波及随机共振长处的前提下,提出并研究了改进双边滤波与随机共振相结合的去噪算法,该算法在优化传统双边滤波算法后引入双稳态随机共振系统,以修复对高频噪声滤除不完全的弊端。仿真结果和实际应用表明：它鲁棒性较强,且去噪能力要高于双边滤波等传统方法。本文算法对随机共振处理图像鲁棒性差等问题进行了改善,对传统双边滤波不能滤除椒盐噪声的缺点进行了优化,使得在提升图像对比度和清晰度上达到更优的处理效果,它在强噪声背景下的图像处理领域具有较好的应用价值。

Stochastic resonance is different from the traditional signal processing method such as the mean filtering,bilateral filtering,which can turn noise into profit to process the weak signal in the nonlinear system under the background of strong noise. In the full use of the advantages of bilateral filtering and stochastic resonance,this paper proposes and researches an image denoising algorithm based on the improved bilateral filtering and stochastic resonance.After optimizing the traditional bilateral filtering algorithm,the algorithm introduces bistable stochastic resonance system to repair the malpractice of filtering high frequency noise.The simulation results and practical application show that： it has strong robustness and its denoising ability is better than bilateral filtering and other traditional methods.This algorithm improves the problems such as the image processing algorithm using stochastic resonance have poor robustness,and optimizes the shortcomings of traditional bilateral filtering can not filter the salt and pepper noise,so that the image contrast and sharpness can be improved.It has good application value in the field of image processing under the background of strong noise.