MATLAB Image Fusion Implementation Using Wavelet Decomposition

Image fusion is a method for integrating features from two source images. This implementation employs wavelet decomposition where both input images are first decomposed into wavelet coefficients using functions like wavedec2() for multi-level decomposition. The algorithm then applies multiple fusion strategies (such as maximum selection, weighted average, or PCA-based methods) to combine approximation and detail coefficients from different frequency bands. The fused coefficients are reconstructed using waverec2() to generate the final enhanced image. This wavelet-based approach significantly improves image processing outcomes by preserving both spatial and frequency domain characteristics, making it suitable for applications like medical imaging (e.g., combining CT and MRI scans) and image enhancement. Key implementation steps include: 1) Wavelet transformation with db4 or sym4 wavelets, 2) Fusion rule application to low-frequency/high-frequency sub-bands, and 3) Inverse wavelet reconstruction. The technique enhances detail visibility and feature representation, ultimately improving image quality and visual effectiveness for diverse application scenarios.