From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising

Reeshad Khan, Ukash Nakarmi, John M. Gauch

2025

Abstract

Deep Neural Networks (DNNs) have revolutionized image denoising, challenging traditional methods such as Stein’s Unbiased Risk Estimator (SURE) and its extensions (eSURE and PURE), along with Extended Poisson Unbiased Risk Estimator (ePURE). These traditional approaches often struggle to generalize across different noise types, especially when noise characteristics are unknown or vary widely, and they are not equipped to handle mixed noise scenarios effectively. In response, we present a novel unsupervised learning strategy that leverages an enhanced diffusion model combined with a dynamically trained Deep Convolutional Neural Network (DnCNN). We introduce adaptive Bayesian loss functions—Bayesian-SURE, Bayesian-PURE, and a newly developed Bayesian-Poisson-Gaussian Unbiased Risk Estimator (Bayesian-PGURE)—that adjust to estimated noise levels and types without prior knowledge. This innovative method enables significant improvements in handling mixed noise conditions and ensures robustness across varied imaging scenarios. Our comprehensive evaluations on MRI data corrupted by Gaussian, Poisson, and mixed noise demonstrate that our approach outperforms existing algorithms, achieving superior denoising performance and image fidelity under diverse, unpredictable conditions. Our contributions advance the state-of-the-art in medical imaging denoising, establishing a new benchmark for unsupervised learning frameworks in managing complex noise dynamics.

Paper Citation

in Harvard Style

Khan R., Nakarmi U. and Gauch J. (2025). From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 547-555. DOI: 10.5220/0013187300003912

in Bibtex Style

@conference{visapp25,
author={Reeshad Khan and Ukash Nakarmi and John Gauch},
title={From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={547-555},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013187300003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising
SN - 978-989-758-728-3
AU - Khan R.
AU - Nakarmi U.
AU - Gauch J.
PY - 2025
SP - 547
EP - 555
DO - 10.5220/0013187300003912
PB - SciTePress