An all-in-one foundation model for multimodal medical image restoration and enhancement

Medical image quality is essential for reliable clinical diagnosis and downstream artificial intelligence analysis, yet routine images are often degraded by noise, motion artefacts, low contrast, low-dose acquisition or modality-specific artefacts.