Wavelet Transform Image Fusion

Multi-scale wavelet transform image fusion is a widely used technique in image processing that combines images from different sensors into a single composite image, thereby enhancing image quality and target recognition capabilities. This method finds applications in medical imaging, remote sensing, and other fields. The implementation typically involves three key stages: First, images are decomposed using wavelet transformation (commonly implemented through functions like wavedec2 in MATLAB). Next, fusion rules (such as maximum coefficient selection or weighted averaging) are applied to the decomposed coefficients. Finally, an inverse wavelet transform (using waverec2 function) reconstructs the fused image. This approach significantly improves image resolution, reduces noise, and enhances detail preservation through its multi-resolution analysis capabilities. Common algorithmic considerations include selecting optimal wavelet bases (e.g., Haar, Daubechies) and designing fusion rules based on frequency domain characteristics.