Three Classical Fusion Algorithms in Image Fusion: Weighted Average, PCA, and IHS

In this article, we briefly introduce three classical algorithms for image fusion: Weighted Average fusion, PCA (Principal Component Analysis) fusion, and IHS (Intensity-Hue-Saturation) fusion. Additionally, we provide a detailed program explanation that is especially useful for those new to image fusion. It's important to note that while these algorithms are classical, their performance may vary across different scenarios; thus, selection should be based on specific application requirements.

For implementation: Weighted Average fusion involves computing a pixel-wise weighted sum of input images, typically using a GUI slider to adjust weight parameters. PCA fusion requires eigenvalue decomposition of the image covariance matrix to determine optimal weighting based on principal components. IHS fusion converts RGB images to IHS color space, replaces the intensity component with a fused version, and converts back to RGB, often employing histogram matching for color preservation.

The provided code includes modular functions for each algorithm, with comments explaining key steps like matrix operations for PCA and color space transformations for IHS. Beginners can experiment with parameters such as fusion weights or component selection to understand algorithm behavior.