MATLAB Implementation of Image Fusion Metrics

Image fusion metrics serve as essential tools in image processing for evaluating image quality. Currently, multiple image fusion metrics have been developed, including comprehensive entropy, spatial frequency, mutual information, and others - totaling 7 distinct evaluation methods. In MATLAB implementation, these metrics typically involve: - Comprehensive entropy calculation using histogram analysis and probability distribution functions - Spatial frequency computation through gradient-based approaches with Sobel or Prewitt operators - Mutual information measurement utilizing joint probability distributions between source and fused images - Additional metrics like structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and edge preservation indices These metrics enable comprehensive evaluation of image processing results by quantifying different aspects of fusion quality, such as information content, texture preservation, and structural consistency. The MATLAB implementation often involves built-in functions like entropy(), imgradient(), and custom algorithms for statistical analysis. Proper implementation requires handling different image formats, normalization procedures, and comparative analysis between source and fused images to ensure accurate quality assessment. By systematically applying these seven metrics, researchers and engineers can significantly improve image processing quality through quantitative performance evaluation and algorithm optimization.