Wavelet Transform-Based Image Fusion Source Code

This article presents a wavelet transform-based image fusion source program with comprehensive technical explanations. The program utilizes wavelet transformation as its core mathematical framework, which decomposes input images into multi-resolution frequency components through wavelet coefficients. This decomposition enables sophisticated image processing and analysis at different scale levels. The primary function of this source code is to merge two or more input images into a single composite image, effectively enhancing both visual quality and informational content. Key implementation features include: - Multi-scale decomposition using discrete wavelet transforms (DWT) - Fusion rule algorithms for combining approximation and detail coefficients - Reconstruction process through inverse wavelet transforms The algorithm operates through three main stages: first performing wavelet decomposition on source images, then applying fusion rules to combine coefficients (typically using maximum selection or weighted average methods), and finally reconstructing the fused image through inverse transformation. This methodology demonstrates particular effectiveness in medical imaging (combining CT/MRI scans), remote sensing (merging multi-spectral data), and security surveillance (integrating different visual perspectives). The provided source code includes optimized functions for wavelet packet decomposition, coefficient processing, and image reconstruction. Implementation typically involves MATLAB's wavelet toolbox functions like wavedec2 (2D decomposition), waverec2 (reconstruction), and custom fusion rules for handling approximation and detail coefficients. This ready-to-use solution significantly improves image processing efficiency and accuracy while advancing development in related technical domains.