## 1 Introduction  
The recent boom in the development of digital technology and multimedia communications has seen digital image analysis methods for nonlinear and nonstationary data receive widespread attention. In digital image processing and analysis in particular, image noise is an important research topic for many different application fields, such as image transmission, image matching, target detection, and remote sensing. However, a wide variety of interference types can disrupt the analysis process. In interference preclusion processes, the existence of noise inevitably leads to image quality degradation, which results in blurring and distortion of valid pixels. Therefore, for digital images, the visual pixel quality must be guaranteed using reliable and effective approaches. Consequently, noise reduction measures are indispensable to maintaining high recognition accuracy and preserving valid pixels for image analysis [1; 2]. To review the existing research works, based on sparse and redundant representations over trained dictionaries, the Bayesian treatment applying K-SVD can yield a state-of-the-art denoising performance [3]. As the digital implementation of new mathematical transformation, the curvelet transform can outperform wavelet methods in image denoising and certain image reconstruction problems [4]. The sparse 3-D transform-domain collaborative filtering can yield a satisfying image denoising effect as a novel image strategy [5]. Block matching 3D denoising (BM3D) is an excellent single-image denoising method [6], and BM3D is a well-engineered algorithm which can achieve the state-of-the-art denoising performance with a plain multi-layer perceptron (MLP) [7]. The pulse-coupled neural network (PCNN) has been studied very extensively. As an effective nonlinear digital data analysis method, it is well capable of isolating noisy pixel points and eliminating high-intensity noise during image processing [8]. In this paper, we are committed to the study of noise reduction effect of PCNN and make improvements to the PCNN method. Initially inspired by the visual cortexes of cats, the PCNN was progressively developed based on synchronous dynamic neuronal activity [9]. From analysis of the acting mechanism, PCNNs actually have the functional characteristics of neuron-specific linear addition, bioelectric pulse transmission through ion channels, nonlinear modulation, and synchronous pulse release [10]. In current digital image processing, PCNNs can be applied to many aspects, including image fusion, image segmentation, and feature extraction. [PERSON] et al. [11] applied a memristor-based circuit implementation of a PCNN with dynamic threshold generators in the digital image processing field. [PERSON] et al. [12] combined a PCNN with the nonsubsampled contourlet (NSCT) model to overcome the coefficients selection problem for the NSCT sub-band that provided an innovative image fusion method based on an image gradient motivation. [PERSON] et al. [13] presented a multifocus image fusion method that was based on robust sparse representation (RSR) and an adaptive PCNN. Many researchers have also conducted relevant studies on image denoising. [PERSON] et al. [14] introduced an adaptive denoising multilayered PCNN for application to salt and pepper noise removal. [PERSON] et al. [15] researched a memristive PCNN (M-PCNN) for medical image denoising processes, which makes the network have a biological function. [PERSON] et al. [16] applied particle swarm optimization (PSO) to the implementation of PCNN parameter optimization. [PERSON] et al. [17; 18] proposed an innovative genetic ant colony algorithm (GACA)-based combination with the PCNN approach to accomplish good quality image denoising. As a preprocessing digital image noise reduction method, the PCNN has several issues that must be resolved to achieve better results. The PCNN is a complex model system that included an input system and a feedback system.