Explore MATLAB source code curated for "pcnn" with clean implementations, documentation, and examples.
This is a custom implementation of image fusion combining NSCT (Non-Subsampled Contourlet Transform) and basic PCNN (Pulse-Coupled Neural Network). The code is designed for clarity and educational purposes, featuring standard PCNN implementation without modifications. Note: Computational complexity may result in slow processing times.
MATLAB-based Image Segmentation using Pulse Coupled Neural Network (PCNN) with Algorithm Implementation Details
Image fusion programs implementing various algorithms including IHS, PCA, Laplacian Pyramid, PCNN, and Wavelet Transform - includes test images, thoroughly debugged and guaranteed to run properly. This represents the carefully developed core work of my graduation thesis.
Adaptive PCNN (Pulse-Coupled Neural Network) fusion provides excellent pixel-level image fusion performance, particularly effective for visible light and infrared image integration.
The combination of PCNN and non-downsampling not only reduces computational time but also enhances system efficiency through optimized feature extraction and multi-resolution analysis.
A MATLAB-based program implementing Pulse-Coupled Neural Networks (PCNN) for image denoising, featuring customizable parameters and neuroscience-inspired processing
MATLAB code implementation of PCNN for image processing applications including denoising, segmentation, and enhancement.
NSCT-PCNN Fusion Framework: Multi-scale decomposition with biological neural network characteristics for high-quality image fusion
PCNN-based image segmentation featuring adaptive iteration control via maximum cross-entropy with MATLAB implementation details
MATLAB code implementation of PCNN with enhanced algorithmic explanations and key function descriptions