Image Fusion Method Based on Wavelet Transform and Local Energy with Performance Evaluation Metrics

This document presents two distinct image fusion methodologies: one based on wavelet transform and local energy analysis, and another utilizing adaptive algorithms. Additionally, we introduce five performance evaluation functions labeled MyFunction1 through MyFunction5 for comprehensive quality assessment. The wavelet transform and local energy-based method represents an effective fusion technique that combines local features from multiple source images to generate enhanced output with improved clarity and detail. The core algorithm involves: 1) Decomposing input images into wavelet coefficients using discrete wavelet transform (DWT), 2) Calculating local energy maps within wavelet subbands to identify significant features, 3) Applying weighted fusion rules where coefficients with higher local energy receive greater emphasis in the reconstructed image. This approach preserves important texture and edge information through multi-resolution analysis. The adaptive algorithm method offers greater flexibility by dynamically adjusting fusion parameters based on image characteristics. The implementation typically includes: 1) Real-time analysis of image attributes like luminance distribution, contrast ratios, and color features, 2) Automatic parameter optimization using machine learning or statistical models, 3) Context-aware fusion rules that adapt to specific image content requirements. This method particularly excels in handling heterogeneous source images with varying exposure conditions or spectral characteristics. Our five evaluation functions (MyFunction1-MyFunction5) provide quantitative metrics for assessing fusion performance. These include: MyFunction1 for objective image quality measurement (peak signal-to-noise ratio, mean squared error), MyFunction2 for information entropy calculation to evaluate preserved information content, MyFunction3 for structural similarity index (SSIM) analysis, with additional functions covering specific aspects like spatial frequency retention and artifact detection. Each function can be implemented as a standalone MATLAB module with standardized input/output interfaces for consistent evaluation. Through systematic application of these evaluation metrics, researchers can perform comprehensive comparative analysis of different fusion techniques, enabling objective performance ranking and algorithm optimization. The combination of these advanced fusion methods and rigorous evaluation framework significantly advances the field of computational image processing and multi-sensor data integration.