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

Reeshad Khan, Ukash Nakarmi, John M. Gauch

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 robus

External IDs:dblp:conf/visigrapp/KhanNG25