Fusion of Two Grayscale Images Using Wavelet Transform

The original content can be modified for more detailed explanation. First, we perform 2-level wavelet decomposition on image 1 using the sym4 wavelet function. The decomposition coefficients undergo fusion processing to enhance轮廓轮廓 outlines while reducing细节细节 details. This involves processing approximation and detail coefficients at different resolution levels. Next, we apply identical 2-level sym4 wavelet decomposition to image 2. The core fusion process occurs in the wavelet transform domain, where we combine coefficients from both images using specific rules - typically selecting maximum values for detail coefficients and weighted averages for approximation coefficients. The final step involves reconstructing the fused coefficients through inverse wavelet transform. Key implementation aspects include: - Using wavedec2() function for 2D wavelet decomposition - Processing coefficient matrices with mathematical operations - Applying waverec2() for reconstruction - Parameter tuning for optimal轮廓轮廓 enhancement and细节细节 reduction These steps enable effective fusion of two grayscale images, achieving improved visual results through wavelet-based multi-resolution analysis.