Jiao Shangbin, Shi Jiaqiang, Wang Yi, Wang Ruijie
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, 710048, China.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an, 710048, China.
Heliyon. 2023 Mar 13;9(3):e14431. doi: 10.1016/j.heliyon.2023.e14431. eCollection 2023 Mar.
In the field of digital signal processing, image denoising is an more and more significant research direction. For the traditional noise reduction theory, noise is considered to be harmful, and the image quality can be improved by analyzing noise characteristics and filtering noise. The appearance of stochastic resonance theory proves that noise can be used to enhance signal, which brings new inspiration to image processing. The classical bistable stochastic resonance model has the problems of high potential barrier and easy saturation, which is not conducive to the improvement of image denoising effect. In this paper, a novel type of stochastic resonance potential well model is quoted, which solves the above shortcomings of the bistable stochastic resonance model, and then combines it with the Gaussian model to propose a composite multistable stochastic resonance model. The dynamic principle of the model in signal detection is described, and the influence of system parameters on image noise reduction is analyzed. The whale optimization algorithm is used to optimize the model parameters, and an adaptive compound multistable stochastic resonance system is established to process pictures and measured radar images under different noise backgrounds. The simulation experiment and engineering application show that the model proposed in this paper solves the problem of high potential barrier and easy saturation of the bistable model, and has better image noise reduction ability compared with Wiener filter, median filter, classical bistable stochastic resonance system and novel type of stochastic resonance potential well system.
在数字信号处理领域，图像去噪是一个越来越重要的研究方向。对于传统的降噪理论，噪声被认为是有害的，通过分析噪声特性并对噪声进行滤波可以提高图像质量。随机共振理论的出现证明了噪声可以用来增强信号，这给图像处理带来了新的启发。经典的双稳随机共振模型存在势垒高和易饱和的问题，不利于图像去噪效果的提升。本文引用了一种新型的随机共振势阱模型，解决了双稳随机共振模型的上述缺点，然后将其与高斯模型相结合，提出了一种复合多稳随机共振模型。阐述了该模型在信号检测中的动力学原理，分析了系统参数对图像降噪的影响。利用鲸鱼优化算法对模型参数进行优化，建立了自适应复合多稳随机共振系统，对不同噪声背景下的图片和实测雷达图像进行处理。仿真实验和工程应用表明，本文提出的模型解决了双稳模型势垒高和易饱和的问题，与维纳滤波器、中值滤波器、经典双稳随机共振系统和新型随机共振势阱系统相比，具有更好的图像降噪能力。
