Image Fusion Method Based on Wavelet Transform and Local Energy

exercise1: This implements an image fusion method based on wavelet transform and local energy analysis. The primary objective is to merge two input images to produce a clearer and more informative composite image. The algorithm typically involves decomposing images using wavelet transforms, calculating local energy maps for feature extraction, and applying fusion rules to combine coefficients from different frequency bands.

exercise2: This presents an adaptive algorithm-based image fusion approach that dynamically adjusts fusion parameters to achieve optimal results. The implementation may include techniques like parameter optimization based on image characteristics, real-time adjustment of fusion weights, or machine learning components that adapt to different image types and quality requirements.

myfunction1~myfunction5: These functions comprise a comprehensive set of performance evaluation metrics for assessing image fusion quality. They calculate various quantitative indicators including image sharpness (using metrics like gradient-based measurements), contrast evaluation (through statistical analysis of pixel intensity distributions), color fidelity preservation (assessing color consistency between source and fused images), and other relevant quality parameters to objectively measure fusion effectiveness.