Region-based Wavelet Transform Fusion Methods

In this document, we discuss region-based wavelet transform fusion methods that primarily utilize regional energy, regional variance, and regional maximum techniques. Regional energy helps identify key features and targets within images through pixel intensity summation calculations (typically implemented using region-wise summation functions), while regional variance measures the uniformity of image areas through statistical dispersion analysis (often computed using variance functions over localized windows). Finally, by optimizing regional maximum values through comparative algorithms across corresponding regions, we achieve more precise image fusion results. The implementation typically involves wavelet decomposition, region segmentation, feature calculation, and fusion rule application. In summary, region-based wavelet transform fusion serves as an effective image processing technique applicable across various scenarios, with core algorithms focusing on multi-scale analysis and region-aware feature integration.