Image denoising algorithm based on impulse neural network (PCNN) and its MATLAB code implementation

Image denoising algorithm based on impulse neural network (PCNN) and its MATLAB code implementation

Image denoising is an important task in the field of computer vision and image processing. Its goal is to restore the original image information from noise-contaminated images. Pulse-Coupled Neural Network (PCNN) is a biologically inspired neural network model that is widely used in image processing for tasks such as edge detection, image segmentation, and image denoising. This article will introduce the image denoising algorithm based on PCNN and provide the corresponding MATLAB code implementation.

The basic principle of the PCNN image denoising algorithm is to simulate the mutual coupling process of neurons in the human visual system. The PCNN model consists of three main parts: input layer, spiking neuron layer and link layer. The input layer receives the pixel values ​​of the image as input signals, the spiking neuron layer simulates the response process of the sensing cells, and the link layer is used to regulate the mutual influence between neurons. The core idea of ​​the PCNN model is to achieve image denoising by simulating the propagation and mutual coupling process of pulses.

The following is the MATLAB code implementation of the PCNN-based image denoising algorithm:

function denoised_image = pcnn_denoising(image, num_iterations, threshold)
    % 参数说明：
    % image：输入的