MedQ-UNI: Toward Unified Medical Image Quality Assessment and Restoration via Vision-Language Modeling

TL;DR

MedQ-UNI introduces a unified vision-language model that jointly assesses and restores medical images across multiple modalities and degradation types, significantly improving performance and interpretability.

Contribution

This work presents the first unified MedQ-UNI model combining medical image quality assessment and restoration using a multimodal architecture trained on a large, annotated dataset.

Findings

Achieves state-of-the-art results across diverse restoration tasks

Generates high-quality, structured quality descriptions

Operates effectively without task-specific tuning

Abstract

Existing medical image restoration (Med-IR) methods are typically modality-specific or degradation-specific, failing to generalize across the heterogeneous degradations encountered in clinical practice. We argue this limitation stems from the isolation of Med-IR from medical image quality assessment (Med-IQA), as restoration models without explicit quality understanding struggle to adapt to diverse degradation types across modalities. To address these challenges, we propose MedQ-UNI, a unified vision-language model that follows an assess-then-restore paradigm, explicitly leveraging Med-IQA to guide Med-IR across arbitrary modalities and degradation types. MedQ-UNI adopts a multimodal autoregressive dual-expert architecture with shared attention: a quality assessment expert first identifies degradation issues through structured natural language descriptions, and a restoration expert then conditions on these descriptions to perform targeted image restoration. To support this paradigm, we construct a large-scale dataset of approximately 50K paired samples spanning three imaging modalities and five restoration tasks, each annotated with structured quality descriptions for joint Med-IQA and Med-IR training, along with a 2K-sample benchmark for evaluation. Extensive experiments demonstrate that a single MedQ-UNI model, without any task-specific adaptation, achieves state-of-the-art restoration performance across all tasks while generating superior descriptions, confirming that explicit quality understanding meaningfully improves restoration fidelity and interpretability.