Medical image fusion is crucial in clinical applications, combining data from various medical imaging modalities into a single high-quality image to enhance diagnosis. However, existing fusion algorithms suffer from several limitations, including inadequate feature extraction, leading to detail loss, poor inheritance of complementary information between modalities, and insufficient evaluation of color information in color and grayscale fusion tasks.