Three Image Fusion Algorithms: IHS, PCA, and Weighted Fusion with MATLAB Implementations

This article provides MATLAB source code implementations for three commonly used image fusion algorithms: Intensity-Hue-Saturation (IHS), Principal Component Analysis (PCA), and weighted image fusion. We will comprehensively explain the underlying principles of each algorithm and present complete source code for practical implementation. The IHS fusion algorithm involves transforming RGB images into IHS color space, where the intensity component is replaced with high-resolution panchromatic data while preserving hue and saturation components. The MATLAB implementation includes color space conversion functions and component replacement logic. PCA-based fusion utilizes principal component analysis to identify the most significant components from multispectral images. The algorithm replaces the first principal component with high-resolution imagery and performs inverse PCA transformation. Our code demonstrates covariance matrix calculation, eigenvalue decomposition, and component substitution techniques. The weighted fusion method applies adaptive weighting coefficients to different image components based on their statistical properties or specific application requirements. The implementation includes weight calculation functions and pixel-level fusion operations. Through this article, you will gain deep understanding of these fusion algorithms and be able to utilize the provided source codes for various image fusion applications. We also include practical examples demonstrating the application of these algorithms with different image types, helping you better understand their performance characteristics and implementation nuances. Each code section contains detailed comments explaining key MATLAB functions and algorithmic steps for easier adaptation to specific projects.