Multi-Resolution Analysis Based Image Fusion Algorithm with Implementation

This package implements image fusion algorithms based on multi-resolution analysis, incorporating region-based fusion methods. These algorithms operate by decomposing images at different resolution levels and then merging their features through wavelet transforms or pyramid decomposition techniques. The implementation typically involves multi-scale decomposition, fusion rule application at each scale, and reconstruction of the fused image. By integrating characteristics and information from multiple source images, these algorithms produce more accurate and comprehensive composite images with enhanced visual quality and information content. The fusion process generally follows three main stages: multi-scale decomposition using transforms like DWT or Laplacian pyramid, coefficient fusion using region-based activity measures or salience maps, and image reconstruction through inverse transforms. Beyond basic image enhancement, these algorithms find applications in specialized domains including medical image processing for combining CT/MRI scans and autonomous driving systems for multi-sensor data integration. Accompanying technical papers provide detailed explanations of the mathematical foundations, algorithmic workflows, and practical implementations of these fusion methods. Key functions in the codebase include multi-scale decomposition routines, fusion rule implementations, and quality assessment metrics. For researchers and developers interested in image fusion, these documented implementations and supporting articles constitute valuable resources for both theoretical understanding and practical application development.