Wavelet Fusion Techniques for Color Images

Wavelet fusion of color images involves merging wavelet coefficients at different scales and orientations, resulting in enhanced image detail and superior visual effects. This technique employs discrete wavelet transform (DWT) algorithms where color channels (RGB/YCbCr) are processed separately or collectively using wavelet decomposition functions like 'wavedec2' in MATLAB. The fusion process typically involves: 1) Decomposing source images into approximation and detail coefficients using wavelet families (e.g., Daubechies, Symlets), 2)Applying fusion rules (e.g., coefficient selection/averaging) to high-frequency components for edge preservation, and 3)Reconstructing the fused image through inverse DWT. Key implementation steps include multi-level decomposition with 'dwt2', coefficient fusion using maximum selection or weighted average methods, and reconstruction via 'idwt2'. Wavelet fusion proves particularly effective in image enhancement, restoration, and analytical applications by extracting comprehensive features from different wavelet domains, enabling more accurate image analysis. This method provides a robust framework for advanced image processing with improved visual outcomes and expanded algorithmic options.