Image Fusion Evaluation Standards with MATLAB Implementation

This text describes MATLAB source code implementation for image fusion evaluation standards. First, we introduce the concept and background of image fusion - a process that combines multiple images into a single composite image to extract useful information from each source, thereby obtaining more comprehensive and accurate visual data. Next, we discuss commonly used image fusion evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). These metrics help quantify the effectiveness and quality of different image fusion algorithms. The MATLAB implementation includes core functions for automatic calculation of these evaluation metrics, featuring efficient matrix operations for image data processing and optimized algorithms for metric computation. For PSNR calculation, the code implements the logarithmic scale conversion of mean squared error between original and fused images. For SSIM, it incorporates luminance, contrast, and structure comparison functions using sliding window techniques. The source code provides modular functions that can be easily integrated into existing fusion pipelines, with detailed comments explaining each computational step. Finally, we include practical example codes demonstrating how to apply these evaluation standards, featuring image preprocessing routines, metric calculation workflows, and result visualization functions. These examples help researchers better understand and apply the standards through hands-on implementation scenarios, complete with error handling and parameter optimization guidance.