Fusion Rule Based on Regional Statistics

Regional statistics-based fusion rule implementation includes complete source code, original source images, and the Contourlet toolbox for direct image processing applications. The algorithm processes multiple input images by analyzing and merging their statistical characteristics within defined regions, generating more accurate fusion results through weighted averaging and variance-based selection methods. Key functions include region segmentation using morphological operations, statistical feature extraction (mean, variance, energy), and fusion decision making based on maximum selection or weighted average rules. These tools are primarily designed for image processing applications such as multi-focus image fusion and medical image integration, but can also be adapted for various data analysis tasks including feature combination and statistical pattern recognition. The implementation provides convenient methods for regional statistical analysis while demonstrating practical usage scenarios for the Contourlet transform's multi-directional and multi-scale decomposition capabilities through directional filter banks and pyramidal decomposition structures.