Wavelet Transform for Image Fusion with 5-Level Decomposition Implementation

The image fusion program employs a 5-level wavelet decomposition approach for processing. Wavelet decomposition represents a fundamental image processing technique that decomposes images into multi-scale frequency domain information, effectively capturing detailed features and characteristics. In our implementation, we utilize wavelet decomposition to break down images into sub-images at different frequency bands, then synthesize these components to generate the final fused image. The algorithm involves using discrete wavelet transform (DWT) functions to perform hierarchical decomposition, where each level separates approximation coefficients (low-frequency components) and detail coefficients (horizontal, vertical, and diagonal high-frequency components). The fusion process typically applies specific rules to combine corresponding coefficients from source images, such as selecting maximum absolute values for detail coefficients and weighted averages for approximation coefficients. This methodology enhances image clarity and contrast while preserving critical detail information. The choice of 5-level decomposition in our implementation ensures optimal multi-resolution analysis, allowing balanced handling of both global features and fine details to improve fusion quality. The program structure includes wavelet transformation functions, coefficient fusion algorithms, and inverse wavelet reconstruction to produce the final output.