Image Fusion Implementation in MATLAB

This represents a comprehensive assignment implementing various image fusion algorithms in MATLAB, including IHS (Intensity-Hue-Saturation), PCA (Principal Component Analysis), weighted average, pyramid-based, and wavelet fusion techniques. Image fusion is a computational technique that merges multiple images into a single composite image to extract useful information from different sources and generate a more comprehensive and accurate representation. The IHS algorithm operates on color space transformation, where input images are decomposed into intensity, hue, and saturation components using MATLAB's rgb2hsv() function. The fusion process involves separately processing these components and reconstructing the final image through inverse transformation with hsv2rgb(). PCA-based fusion employs principal component analysis to transform input images into orthogonal feature vectors using MATLAB's pca() or svd() functions. The algorithm selects dominant components through eigenvalue analysis and combines them to preserve maximum variance information. Weighted average fusion implements pixel-level blending using arithmetic operations, where different weighting coefficients can be applied to source images through matrix multiplication and summation operations. Pyramid fusion techniques involve multi-scale decomposition using Laplacian or Gaussian pyramids constructed with impyramid() function, followed by coefficient fusion at different resolution levels and pyramid reconstruction. Wavelet fusion utilizes wavelet decomposition through wavedec2() function to separate images into approximation and detail coefficients at multiple scales. The fusion rules typically involve selecting maximum absolute coefficients or energy-based selection criteria before reconstruction using waverec2(). These algorithms find extensive applications in image processing for quality enhancement, feature extraction, and visual improvement, with MATLAB implementations typically involving image preprocessing, transformation domain processing, fusion rule application, and inverse transformation stages.