Image Fusion of Two Images Using Laplacian Transform

Image fusion technology can integrate complementary information from multiple images into a single image, and methods based on Laplacian transform have become classical solutions due to their excellent frequency domain processing capabilities. This technique primarily utilizes the Laplacian pyramid to perform multi-scale decomposition of source images, achieving selective fusion at different frequency bands.

The implementation process begins by constructing a Gaussian pyramid for the input images, where each layer is obtained through downsampling to represent images at different scales. Subsequently, the Laplacian pyramid is computed based on the Gaussian pyramid—this difference pyramid effectively captures detailed features at various scales. The core step involves designing fusion rules; typically, a maximum-value strategy is applied to high-frequency components to preserve edge textures, while a weighted average is used for low-frequency components to maintain overall brightness balance. In code implementation, this can be achieved by comparing pixel-wise gradients or variance in high-frequency bands and applying linear blending for low-frequency coefficients.

During the reconstruction phase, the fused results from each layer are integrated into the final output through inverse pyramid operations. This method significantly enhances the information content of fused images, particularly in medical imaging and remote sensing image processing, where it can preserve key features from multiple source images simultaneously. Experimental results show that, compared to simple averaging methods, Laplacian transform-based fusion better preserves image details and suppresses artifacts, with the algorithm effectively handling edge preservation through multi-resolution analysis.