MATLAB Implementation of PCNN Algorithm for Image Noise Removal

This program utilizes the PCNN (Pulse-Coupled Neural Network) algorithm implemented on the MATLAB platform to remove noise from images. The algorithm is neuroscience-inspired, simulating the human brain's response to external stimuli. By feeding image data into the algorithm, the program automatically detects and eliminates noise while preserving image details through iterative pulse-coupled neuron firing mechanisms. The implementation includes key functions for image preprocessing, neuron linking, and threshold adaptation that mimic biological visual processing. Users can modify algorithm parameters such as linking strength, decay factors, and iteration counts to optimize denoising performance for different noise types and image characteristics. The code structure allows for easy integration of various noise models and supports both grayscale and color image processing. This MATLAB-based PCNN implementation provides an efficient and accurate denoising solution for image processing applications, particularly effective for salt-and-pepper noise and Gaussian noise removal while maintaining edge preservation and texture details. The object-oriented design enables straightforward parameter tuning and performance evaluation through built-in quality metrics like PSNR and SSIM calculations.