Degradation-Aware Routing Network with Structure-Aware Cross-Modal Priors for All-in-One Medical Image Restoration

Medical image restoration (MedIR) involves heterogeneous modalities with distinct imaging characteristics, degradation processes, and restoration objectives. Learning a unified model is challenging because shared representations must accommodate modality-dependent structures and task-specific degradations. We propose DRSCP-Net, an all-in-one MedIR framework coupling a degradation-aware routing network with structure-aware cross-modal priors. The framework follows two training stages. First, Structure-Aware Vision–Language Adaptation (SAVLA) extends Degradation-Aware CLIP with edge-aware structural modeling and uses medical image–text contrastive learning to obtain structure-sensitive degradation representations. Second, a Degradation-Aware Routing Instruction Mechanism (DARIM) combines image features with a learnable instruction dictionary to generate expert-selection instructions, while deriving global and layer-specific prompts from the image features. Within Prompt-Guided Expert Selection Transformer (PGES-Former) blocks, routing instructions determine expert activation, and degradation representations from SAVLA are incorporated through cross-attention followed by hierarchical prompt-conditioned gating. At the bottleneck, Context-Gated Dual-Domain Modulation (CGDM) integrates spatial and frequency-domain features under global prompt guidance, whereas Adaptive Gated Fusion (AGF) controls feature transfer across encoder–decoder skip connections. No text input is required during restoration. Experiments on MRI super-resolution, CT denoising, and PET synthesis demonstrate competitive performance in both task-specific and all-in-one settings. In the all-in-one setting, DRSCP-Net achieves an average PSNR of 34.37 dB, tying for the highest among the compared methods, and obtains the best or tied-best results on three of the six task-wise metrics. Joint training yields positive transfer on all three tasks, and downstream segmentation exhibits improved average agreement with reference masks generated from high-quality images. Code will be made available at https://github.com/fanamber831/DRSCP-Net}{\textcolor{red}{https://github.com/fanamber831/DRSCP-Net.