MATLAB Implementation of Wavelet Transform for Image Fusion

This code implements image fusion using wavelet transform. During implementation, the wavelet analysis toolbox wavemenu is utilized, with the wavelet analysis function wavedec2() performing 2D discrete wavelet decomposition. Wavelet transform is a signal processing technique that decomposes signals into sub-signals of different frequencies, effectively revealing signal characteristics and structures. For image fusion applications, wavelet transform combines features from two source images to create a composite image with richer informational content. The implementation follows this workflow: First, wavedec2() decomposes each input image into approximation coefficients (low-frequency components) and detail coefficients (horizontal, vertical, and diagonal high-frequency components) at multiple resolution levels. The fusion algorithm then selectively combines corresponding coefficients from both images - typically using averaging for approximation coefficients and maximum selection rules for detail coefficients to preserve edge information. Finally, the inverse wavelet transform reconstructs the fused image from the modified coefficients. Wavelet transform finds extensive applications in image processing, particularly in image fusion domains where it outperforms traditional methods by preserving both spectral information and spatial details. Through this implementation, users can gain deeper understanding of wavelet transform principles and practical applications in multidimensional signal processing.