MATLAB Source Code for Image Fusion Implementation

This MATLAB program provides ready-to-run source code for implementing image fusion techniques. The codebase supports multiple fusion methodologies including pixel-level, feature-level, and decision-level fusion approaches. Key algorithms implemented include wavelet transform fusion, principal component analysis (PCA) based fusion, and pyramid decomposition methods. The main workflow typically involves: 1. Loading and preprocessing input images using imread() and im2double() functions 2. Implementing fusion algorithms through customized functions handling transform domains 3. Applying fusion rules such as maximum selection, weighted average, or neural network-based blending 4. Reconstructing the fused image using inverse transforms Notable functions include: - wavelet_fusion(): Implements discrete wavelet transform (DWT) with coefficient fusion - pca_fusion(): Utilizes principal component analysis for feature weighting - pyramid_fusion(): Employs Laplacian/Gaussian pyramid decomposition The code is modularly structured with separate functions for different fusion techniques, allowing users to easily modify parameters and test various approaches. Preprocessing modules handle image registration and intensity normalization, while post-processing modules include quality assessment metrics like entropy evaluation and structural similarity index. This implementation is particularly valuable for applications in medical imaging (combining CT/MRI scans), remote sensing (multispectral satellite image fusion), and computational photography. Users should select appropriate fusion methods based on their specific data characteristics and application requirements, with documentation provided for parameter optimization guidance.