Wavelet-Based Image Fusion Algorithm with MATLAB Implementation

The MATLAB-based wavelet image fusion algorithm produces remarkable results in image processing applications. This algorithm achieves image fusion by performing wavelet transforms on two or more input images and effectively combining their distinct characteristic information. Implementation typically involves decomposing source images using wavelet functions like 'db4' or 'sym4' through MATLAB's wavedec2 function, followed by fusion rule application in wavelet coefficient domains. The algorithm employs different fusion strategies for approximation coefficients (typically weighted averaging) and detail coefficients (commonly maximum selection or activity-level measurement) to preserve important features from each source image. Wavelet image fusion finds extensive applications across various domains including medical image processing (combining CT and MRI scans), remote sensing imagery (merging panchromatic and multispectral data), and security surveillance systems. The MATLAB implementation utilizes functions such as wfusimg for integrated fusion operations or custom scripts using wavelet toolbox functions for precise control over fusion rules. Key advantages include enhanced image quality through improved detail preservation, noise reduction via wavelet thresholding techniques, and adaptability to specific application requirements through customizable fusion parameters. The MATLAB environment provides excellent visualization capabilities using imshow and montage functions to compare pre-fusion and post-fusion results, ensuring optimal outcomes for diverse imaging needs.