# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 1 Introduction  
Image is one of the important sources for people to obtain information [1]. The image data has become a vital and irreplaceable information carrier in the fields of science and technology [2], industry [3], aerospace [4], medical care [5], education [6], etc. However, in the process of generation, acquisition and transmission, the image is often disturbed by noise, which causes image quality damage, image visual effect reduction, and even seriously affects the effective transmission of information [7]. Therefore, it is of great significance for image detection and information recognition to study image denoising algorithms under strong noise [8]. At present, the classical denoising methods mainly include mean filtering, Wiener filtering, median filtering, Gaussian filtering, etc. [9]. However, In the past decade, scholars have found that the classical image denoising methods do not perform well in a strong noise environment [10]. The application of stochastic resonance (SR) theory in image processing provides another idea for image denoising [11]. SR theory can use noise energy to enhance image signal, so as to filter out noise without damaging the original image. Therefore, the image denoising method based on the SR principle has been widely studied by scholars [12]. SR was first proposed in 1981 by [PERSON] et al. in the study of glaciers in Earth's ancient meteorology [13]. With the in-depth study of stochastic resonance theory, it has been applied more and more widely, including in the field of image processing. [PERSON] et al. found that the combination of SR mechanism and Radon transform could extract the linear edges of objects buried in strong noise images [14]. [PERSON] et al. applied adaptive SR to denoising binary images in strong noise, and achieved a satisfactory peak signal-to-noise ratio in the experiment [15]. [PERSON] et al. proposed a signal processing method based on the best matching method and array SR in 2016. They improved the processing effect of array SR and analyzed the bit error rate of the system output signal [16]. With the continuous exploration and improvement of SR mechanism in weak signal detection and enhancement, it provides a steady stream of new technologies for image denoising in the field of image processing [17]. The SR mechanism makes the input signal, noise and nonlinear system achieve the best match by adjusting the structural parameters of the system. However, selection of SR structural parameters is always a difficult problem, which has a direct impact on the output of the system [18]. The adaptive SR parameters has been a research hotspot in this field [19]. At present, many researchers selecting structural parameters of SR used intelligent optimization algorithms [20], but the optimization results are prone to fall into local optimization and low computational efficiency [21]. Therefore, when optimizing the parameters of stochastic resonance, it is very important to select an appropriate intelligent optimization algorithm for image enhancement. Intelligent optimization algorithm is an intelligent technology that has emerged in recent years. It can solve complex problems in practical projects by simulating the cooperation and competition among biological groups. With the continuous development of intelligent optimization algorithms, scholars at home and abroad have proposed many new intelligent optimization algorithms. [PERSON] et al. proposed a multi-label feature selection method based on ant colony optimization (ACO) [22], and then proposed a novel multi-label correlation redundancy feature selection method based on ACO [23]. [PERSON] et al. proposed a multi leader particle swarm optimization [24]. [PERSON] et al. proposed a grouped GSA algorithm [25]. [PERSON] et al. proposed an improved whale optimization algorithm based on average swarm intelligence [26]. While optimization algorithm (WOA) [27] is a new intelligent optimization algorithm proposed by [PERSON] et al. to simulate the hunting behavior of a humpback whale bubble net.