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

This article introduces an image fusion program capable of implementing three different fusion algorithms: Weighted, IHS, and PCA. The Weighted fusion algorithm blends images from different spectral bands by assigning specific weights to each pixel, typically implemented through pixel-wise linear combinations where weight coefficients can be optimized based on band characteristics. The IHS fusion algorithm transforms three input bands into Intensity, Hue, and Saturation components using color space conversion, then performs fusion in the IHS domain before converting back to RGB space - this method effectively preserves spectral characteristics while enhancing spatial details. The PCA fusion algorithm employs Principal Component Analysis to merge multi-band information by identifying orthogonal components that capture maximum variance, where the first principal component typically contains the most significant information for fusion. These algorithms enable effective integration of multi-band images, significantly improving overall image quality through complementary information extraction and noise reduction techniques.