Image Fusion - Laplacian Pyramid (LP) Image Fusion with Toolbox Implementation

This article introduces the concept of image fusion and provides comprehensive details about Laplacian Pyramid (LP) based image fusion methodology. The implementation utilizes a multi-scale decomposition approach where source images are decomposed into different frequency bands using Gaussian and Laplacian pyramids. The fusion process involves combining corresponding pyramid levels through weighted averaging or maximum selection rules, followed by pyramid reconstruction to generate the final fused image. We provide a complete toolbox and ready-to-execute code (available in both MATLAB and Python versions) that implements the LP fusion algorithm. The code includes key functions for pyramid construction (gaussian_pyramid, laplacian_pyramid), fusion rule implementation (fusion_rules), and pyramid reconstruction (reconstruct_pyramid). Each function contains detailed comments explaining the algorithmic steps and parameter configurations. The article further explores practical applications of these tools, demonstrating how to adapt the code for different image types (medical, remote sensing, multi-focus) and optimize parameters for specific use cases. We include several case studies showing real-world applications in medical imaging (combining CT and MRI scans), remote sensing (pan-sharpening multispectral images), and photography (multi-focus image fusion). Each case study provides code snippets showing how to preprocess input images, adjust fusion parameters, and evaluate results using quality metrics like PSNR and SSIM. Finally, we discuss advanced implementations including adaptive fusion rules and multi-modal fusion techniques, with code examples demonstrating how to extend the basic LP framework. The complete toolbox allows researchers and practitioners to immediately apply these techniques while understanding the underlying mathematical principles and implementation details.