MATLAB Image Fusion Implementation with Algorithmic Approaches

Image fusion programs are computational implementations designed to merge multiple source images into a single composite image. These programs employ various image processing techniques such as pixel-level fusion algorithms (where corresponding pixels from input images are combined using arithmetic operations or weighted averages), wavelet transform-based methods (utilizing multi-resolution analysis through functions like wavedec2 and waverec2 in MATLAB for frequency domain fusion), and deep learning approaches (implementing convolutional neural networks with frameworks like MATLAB's Deep Learning Toolbox for feature extraction and fusion). Research-oriented image fusion programs serve as essential tools in academic and industrial research domains. By developing novel fusion algorithms and publishing scientific papers, researchers contribute to advancing image fusion technology, establishing foundations for further exploration in related fields. Consequently, both practical image fusion programs and their research counterparts play crucial roles in computer vision and image processing domains, providing extensive opportunities to understand and innovate image fusion methodologies through systematic code implementation and algorithmic optimization.