MATLAB Code Implementation for Image Fusion

Image fusion programs refer to specialized software or algorithms used to extract high-resolution components from images, thereby achieving image clarification. These programs typically employ multi-scale decomposition techniques such as wavelet transforms or pyramid decomposition to separate high-frequency details from low-frequency background information. Through image fusion programs, we can effectively combine low-resolution images with high-resolution counterparts to produce clearer, more detailed results. Key implementation aspects include spatial domain fusion methods (like weighted averaging) and transform domain approaches (such as Discrete Wavelet Transform) that preserve edge information while reducing noise. The application domains of image fusion programs are extensive, covering computer vision, medical image processing, remote sensing image analysis, and beyond. Using image fusion programs enables better image understanding and analysis, leading to more accurate conclusions and decisions. Common MATLAB functions for implementation include imread() for image input, dwt2() for wavelet decomposition, and imfuse() for basic fusion operations, often complemented by custom fusion rules for optimal results.