MATLAB Image Fusion: Algorithms and Implementation Techniques

The application domains of image fusion are progressively expanding, yet research in this field both domestically and internationally lacks systematic organization and a complete theoretical framework. Although current research directions remain somewhat limited, numerous potential research avenues exist in both theoretical and engineering applications. For instance, further investigation could focus on image fusion applications in medical imaging, remote sensing, and security surveillance systems. The implementation includes MATLAB functions for multi-scale decomposition (e.g., dwt2 for discrete wavelet transform) and fusion rule optimization using weighted averaging and maximum selection methods. As image processing technologies continue to evolve, image fusion research should increasingly intersect with related fields such as machine learning and artificial intelligence. The code demonstrates potential integration points with neural networks for adaptive fusion weight determination and feature-based fusion rule optimization. With deeper research exploration, image fusion algorithms are expected to become more stable and efficient. Beyond the MATLAB programs provided in this repository, researchers can further explore other programming languages and tools (such as Python with OpenCV or TensorFlow) to expand the scope and depth of image fusion studies. The implementation includes comparative analysis scripts evaluating fusion performance metrics like mutual information and structural similarity index.