MATLAB Code Implementation for Image Fusion

Develop MATLAB code to achieve image fusion while ensuring the generated image maintains high-quality visual effects. Image fusion is the process of combining multiple images into a single composite image that contains more information and improved visual characteristics. By fusing multiple images, the optimal features from each source image can be extracted to produce more accurate and clearer results. During code implementation, various image fusion algorithms can be employed, such as weighted average method, Laplacian pyramid fusion, and other advanced techniques to achieve optimal fusion results. The implementation typically involves using MATLAB's Image Processing Toolbox functions like imfuse for basic fusion operations or custom implementations using matrix operations for weighted blending. For pyramid-based methods, functions like impyramid can be utilized to create multi-resolution representations. Additionally, image parameters including brightness, contrast, and saturation can be adjusted through MATLAB's color correction functions like imadjust or histeq to further enhance the visual quality. Finally, by comparing the fused image with the original source images using quantitative metrics (PSNR, SSIM) or visual assessment, the performance and effectiveness of the fusion algorithm can be properly evaluated.