Abstract
Medical image restoration is essential for improving the usability of noisy, incomplete, and artifact-corrupted clinical scans, yet existing methods often rely on task-specific retraining and offer limited control over the trade-off between faithful reconstruction and prior-driven enhancement. This lack of controllability is especially problematic in clinical settings, where overly aggressive restoration may introduce hallucinated details or alter diagnostically important structures. In this work, we propose CARE, a training-free controllable restoration framework for real-world medical images that explicitly balances structure preservation and prior-guided refinement during inference. CARE uses a dual-latent restoration strategy, in which one branch enforces data fidelity and anatomical consistency while the other leverages a generative prior to recover missing or degraded information. A risk-aware adaptive controller dynamically adjusts the contribution of each branch based on restoration uncertainty and local structural reliability, enabling conservative or enhancement-focused restoration modes without additional model training. We evaluate CARE on noisy and incomplete medical imaging scenarios and show that it achieves strong restoration quality while better preserving clinically relevant structures and reducing the risk of implausible reconstructions and show that it achieves strong restoration quality while better preserving clinically relevant structures and reducing the risk of implausible reconstructions. The proposed approach offers a practical step toward safer, more controllable, and more deployment-ready medical image restoration.
Abstract (translated)
医学图像恢复对于提升含噪声、不完整及存在伪影的临床扫描图像的可用性至关重要，然而现有方法往往依赖任务特定的重训练，且在忠实重建与先验驱动增强之间的权衡上控制有限。这种缺乏可控性的问题在临床环境中尤为突出，因为过于激进的恢复可能引入幻觉细节或改变诊断关键结构。在本研究中，我们提出了CARE——一种面向真实世界医学图像的无训练可控恢复框架，能在推理过程中显式平衡结构保持与先验引导的细化。CARE采用双潜变量恢复策略：其中一个分支 enforcing 数据保真度与解剖一致性，另一分支则利用生成先验恢复缺失或退化信息。其风险感知自适应控制器能根据恢复不确定性与局部结构可靠性动态调整各分支贡献，从而在无需额外模型训练的情况下实现保守型或增强导向型恢复模式。我们在含噪声和不完整的医学成像场景中评估了CARE，结果表明该方法在实现高质量恢复的同时，能更好保留临床相关结构并降低不合理重建风险。所提出的方法为更安全、更可控且更易部署的医学图像恢复提供了实用进展。
URL
https://arxiv.org/abs/2603.25026
