MATLAB Implementation of Image Fusion Techniques

In this paper, we explore various methodologies for image fusion. Image fusion involves combining images from different sensors or sources to produce more accurate and comprehensive imagery. The high-pass filtering technique preserves high-frequency components using specialized filters, which can be implemented in MATLAB using functions like fspecial() for filter creation and imfilter() for application. The IHS (Intensity-Hue-Saturation) transformation method separates images into three components: intensity (I), hue (H), and saturation (S), achieved through rgb2hsv() conversion followed by component substitution. Principal component analysis (PCA) performs fusion by computing dominant eigenvectors through MATLAB's pca() function or cov() and eig() for covariance matrix decomposition. Wavelet-based approaches employ multi-scale decomposition using functions such as wavedec2() for 2D discrete wavelet transform, enabling fusion at different resolution levels. The hybrid wavelet-IHS method integrates both techniques by first applying IHS transformation, then performing wavelet decomposition on the intensity component, with reconstruction via waverec2(). These methodologies collectively enhance fusion quality, yielding more precise and complete image information through strategic algorithm combination.