## 1 Introduction
In many cases, the predefined network parameters mean that the researchers must try to select suitable parameters, many times, contemporaneously, in response to different input images. To address the above issues, many methods, including simplified models and intelligent swarm models, are used to improve the image denoising performance. [PERSON] et al. [19] proposed a simplified PCNN method with adaptive parameters that was excellent in terms of both visual appearance and parameter setting. [PERSON] et al. [20] applied a cuckoo search (CS) algorithm based on a simple PCNN model to a multi-parameter optimization problem. [PERSON] et al. [21] introduced a quantum-behaved particle swarm optimization (QPSO) model to optimize the PCNN model with good effect. It should be pointed out that the existing related optimization algorithms that are introduced for setting PCNN parameters have several common disadvantages that must be resolved, including complex search mechanisms, easy relapse into local optimum behavior, and low generalization capabilities. In addition, the fitness functions of these optimization methods must be modified to promote both search efficiency and generalization ability. A fast and accurate parameter optimization is a hot issue that needs to be resolved in PCNN, which is also a major content of this paper. Moreover, analysis of whole image denoising processes shows that the optimized PCNN combined with filtering can produce a particular noise reduction effect. However, when faced with high-intensity noise, the entire noise reduction process becomes challenging [22]. Some of the noise will always be left over and will affect image clarity and detail recognition. Furthermore, in the current situation, most noise suppression methods that play a good role in low intensity noise include conventional filters such as a single median filter. How to settle the contaminated complicated characteristics and incomplete noise suppression puzzles caused by high-intensity noise are hot spot research topics. Additionally, in the field of image noise reduction, how to keep as much image detail as possible and restore the original image information for use, are also major problems. Motivated by the above analysis, we focus on the improvement of the noise reduction effect of PCNN images denoising. To overcome the limitation of the existing PCNN parameter optimization problem and meet better noise reduction requirements, an adaptive PCNN model in which the PCNN parameters are optimized using the grey wolf optimization (GWO) algorithm to address the parameter issues described above. As a meta-heuristic optimization technique, the GWO has merits that include simplicity, flexibility, a derivation-free mechanism, and avoidance of local optima [23]. Under its leadership hierarchy and hunting mechanism, the GWO is capable of filtering, completing iteration and optimization of a series of parameters [24]. At the same time, the GWO provides high convergence speeds and high optimization accuracy. After the Experiments, GWO is applicable to challenging problems with unknown search spaces [25]. Furthermore, for high-intensity noise suppression, further research about bidimensional empirical mode decomposition (BEMD) is introduced in an innovative way. BEMD is actually very widely used in image processing, such as image denoising, and is an extension of [PERSON]'s empirical mode decomposition (EMD) method [26]. The high-intensity contaminated noise will be very concentrated and dense in the polluted image. BEMD is applied to decompose the heavily contaminated image into several bidimensional intrinsic mode functions (BIMFs). In this way, high-intensity noise can be dispersed into smaller components, and use of the BEMD effectively accomplishes an image-adapted decomposition procedure to provide image-noise separation. The decomposed image components are then more compatible with the processing conditions required for the optimized PCNN method.