YUV-based SVD-VGG hybrid fusion for multimodal MRI-PET image integration
Kandala S.S.V.V. Ramesh, S. Selva Kumar
TL;DR
This paper introduces a new method for combining MRI and PET brain images using a hybrid approach that improves detail and color while handling noise.
Contribution
The novel SVD–VGG hybrid fusion framework integrates luminance decomposition and feature enhancement for noise-aware multimodal image fusion.
Findings
The proposed method achieves high structural fidelity and perceptual quality in fused images.
It maintains sub-second runtime while preserving color and contrast under synthetic noise conditions.
Quantitative metrics like PSNR, SSIM, and LPIPS show consistent performance improvements.
Abstract
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 (σ2=0.25) 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 256×256 for fusion and 224×224 for feature extraction. Quantitative evaluation using PSNR, SSIM, CC, and perceptual LPIPS indicates that the proposed method achieves consistent structural fidelity, perceptual…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Image Enhancement Techniques · Advanced Image Processing Techniques