Wavelet Transform for Each Image Channel

This image processing procedure can be implemented through the following computational steps:

1. For each input image channel, apply discrete wavelet transformation (DWT) using functions like wavedec2() in MATLAB or PyWavelets in Python. This decomposition extracts high-frequency components along horizontal, vertical, and diagonal (45-degree) orientations, plus the low-frequency approximation component, forming a multi-resolution wavelet pyramid structure for each source image.

2. Implement fusion logic by comparing corresponding feature maps across different pyramid levels of both images. The algorithm selects coefficients using maximum absolute value criteria (max-rule fusion) through element-wise comparison operations like numpy.maximum() or MATLAB's max function, preserving dominant features from both sources.

3. Reconstruct the final fused image by performing inverse wavelet transformation (IDWT) using functions like waverec2() on the combined coefficient sequence. This synthesis process merges the selected high-frequency details and low-frequency base components into a coherent output image with enhanced visual characteristics.

This wavelet-based fusion methodology effectively preserves salient features from multiple sources while maintaining image structural integrity, resulting in superior visual quality compared to simple blending techniques.