Medical image fusion is an important computer-aided technique that combines critical elements from diverse medical pictures to improve diagnostic detail and accuracy. Despite recent advances, many fusion systems fail to provide enough information and textures for accurate disease identification, generally due to insufficient noise removal from the original medical image. In response to these problems, we offer a unique fusion technique designed for multimodal medical imaging. Our method uses fidelity-driven optimized (FDO) reconstruction to maintain important information while reducing noise impacts. Furthermore, we present a rank-coefficient optimization strategy for reducing the impact of noise on multiple mediums medical imaging Furthermore, we recommend the use of a repetitive detail preservation strategy to incorporate extra data as well as textures in the initial multimodal medical images while retaining high resilience. The final product demonstrates the capabilities of our innovative fusion technology. Extensive studies have proved the resilience of our technique, notably in dealing with noisy medical images, highlighting its potential for use in diagnostic applications.