Multimodal medical image fusion enhances diagnostic interpretation by integrating anatomical and functional information into a single image. This work proposes an efficient hybrid framework, termed SVD-VGG Hybrid Fusion, unifying Singular Value Decomposition (SVD) for luminance decomposition and a lightweight VGG-based feature extractor for high-frequency enhancement. Synthetic Gaussian noise (Formula: see text) is added to MRI and Poisson noise to PET images to simulate representative acquisition degradations, while the SVD and VGG-based feature paths strengthen structural detail and functional contrast. Experiments were conducted on a single public brain dataset with image pairs resized to Formula: see text for fusion and Formula: see text for feature extraction. Quantitative evaluation using PSNR, SSIM, CC, and perceptual LPIPS indicates that the proposed method achieves consistent structural fidelity, perceptual quality, and color preservation while maintaining sub-second runtime per case. Although evaluated only on brain MRI-PET data and under synthetic noise conditions, the results suggest that the SVD-VGG hybrid design provides a noise-aware and color-preserving fusion strategy suitable for practical multimodal image fusion workflows.
Ramesh et al. (Tue,) studied this question.