DNA-Prior: Unsupervised Denoise Anything via Dual-Domain Prior
This addresses the challenge of robust denoising in clinical environments with heterogeneous modalities and limited ground-truth data, though it is incremental as it builds on prior unsupervised and hybrid methods.
The paper tackles the problem of medical image denoising without requiring annotated datasets by introducing DNA-Prior, an unsupervised framework that integrates implicit and explicit priors, achieving consistent noise suppression and structural preservation across multiple modalities.
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.