MoDeFold: Modal-Degradation Joint Unfolding for All-in-One Medical Image Restoration

All-in-one medical image restoration (MedIR) seeks a single model for heterogeneous, modality-coupled degradations, including k-space undersampling, low-dose noise, and limited-photon degradation. Existing Deep Unfolding Networks (DUNs) do not fully account for the interaction between modality-dependent content and heterogeneous degradations within a shared restoration pipeline. Across modalities, differences in tissue contrast and appearance are further mixed with corruption, making reliable content representations difficult to obtain. We propose Modal-Degradation unFolding (MoDeFold), a unified unfolding framework that reconciles divergent modality and degradation distributions for all-in-one MedIR. MoDeFold models modality-dependent content and modal-degradation embedding as complementary priors for a shared unfolding process. Our core insight is to use the modal-degradation prior to enhance modality-specific content features, then combine the enhanced features with the modal-degradation prior to jointly control the unfolding process. A multi-scale vector-quantized autoencoder supplies content features supervised by clean-code references, a Modal-Degradation Embedder trained with contrastive learning provides the joint prior, and embedding-conditioned modulation and fusion connect the two. Experiments demonstrate that MoDeFold outperforms baselines in all-in-one restoration, achieving an improvement of 1.126 dB in average PSNR, with consistent gains under single-task setting.