Nuclear Diffusion Models for Low-Rank Background Suppression in Videos
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
Video sequences often contain structured noise and background artifacts that obscure dynamic content, posing challenges for accurate analysis and restoration. Robust principal component methods address this by decomposing data into low-rank and sparse components. Still, the sparsity assumption often fails to capture the rich variability present in real video data. To overcome this limitation, a hybrid framework that integrates low-rank temporal modeling with diffusion posterior sampling is proposed. The proposed method, Nuclear Diffusion, is evaluated on a real-world medical imaging problem, namely cardiac ultrasound dehazing, and demonstrates improved dehazing performance compared to traditional RPCA concerning contrast enhancement (gCNR) and signal preservation (KS statistic). These results highlight the potential of combining model-based temporal models with deep generative priors for high-fidelity video restoration.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 9 of its 18 references that are in the map. First posted on arXiv 25 Sep 2025.
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- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
- 2024A Survey on Diffusion Models for Inverse Problemscited by 180
- 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 44
- 2024Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learningcited by 35
- 2021Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detectioncited by 34
- 2025Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imagingcited by 2
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- 2024Ultrasound Imaging based on the Variance of a Diffusion Restoration Model
- 2024Diffusion Posterior Proximal Sampling for Image Restoration
- 2023Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty
- 2024Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction
- 2025LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restoration
- 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
- 2023Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
- 2023Generative Diffusion Prior for Unified Image Restoration and Enhancement