Image Fusion Based on Wavelet Transform

Wavelet transform-based image fusion is a prominent image processing technique that effectively combines information from multiple source images to generate a composite image with enhanced detail richness and improved clarity. This methodology implements six distinct regional algorithms, including the XXX algorithm, YYY algorithm, ZZZ algorithm, among others. These algorithms can be selectively applied based on specific image characteristics and application requirements to achieve optimal fusion results. The wavelet decomposition enables multi-resolution analysis, where fusion operations are typically performed at multiple scales using functions like wavedec2() for decomposition and waverec2() for reconstruction in MATLAB implementations. Through wavelet processing, the fusion technique better preserves image details and structural information by separately handling approximation coefficients (low-frequency components) and detail coefficients (high-frequency components) at different decomposition levels. This approach significantly improves image quality and visual perception, making wavelet-based image fusion widely adopted and extensively researched in various image processing applications such as medical imaging, remote sensing, and computer vision.