无需标注数据，通用去噪框架可自动修复各类医学影像噪声。
DNA-Prior: Unsupervised Denoise Anything via Dual-Domain Prior
- 用双域先验融合网络结构与频域空间约束，实现无监督图像重建。
- 跨模态实验中在不同噪声下均保持结构清晰，去噪效果稳定。
- 适合缺乏标注数据的临床场景，尤其适用于多模态医学成像。
医学影像处理依赖于稳健的去噪能力以保障分割与重建等下游任务。然而，现有去噪方法多依赖大规模标注数据或有监督学习，在临床环境中因模态异质性及真实标签稀缺而受限。为此，我们提出DNA-Prior，一种通用的无监督去噪框架，通过数学上严谨的混合先验，直接从含噪观测中重建干净图像。该框架整合（i）通过深度网络参数化施加的隐式架构先验，以及（ii）由频域保真项与空间正则化函数构成的显式谱-空域先验。这种双域联合优化机制能同时保留全局频域特征与局部解剖结构，且无需外部训练数据或模态特异性调参。跨多种模态的实验表明，DNA-Prior在多样噪声条件下均实现一致的降噪与结构保持效果。
原文摘要 · Abstract (English)
Medical imaging pipelines critically rely on robust denoising to stabilise downstream tasks such as segmentation and reconstruction. However, many existing denoisers depend on large annotated datasets or supervised learning, which restricts their usability in clinical environments with heterogeneous modalities and limited ground-truth data. To address this limitation, we introduce DNA-Prior, a universal unsupervised denoising framework that reconstructs clean images directly from corrupted observations through a mathematically principled hybrid prior. DNA-Prior integrates (i) an implicit architectural prior, enforced through a deep network parameterisation, with (ii) an explicit spectral-spatial prior composed of a frequency-domain fidelity term and a spatial regularisation functional. This dual-domain formulation yields a well-structured optimisation problem that jointly preserves global frequency characteristics and local anatomical structure, without requiring any external training data or modality-specific tuning. Experiments across multiple modalities show that DNA achieves consistent noise suppression and structural preservation under diverse noise conditions.
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