  The application of Deep Neural Networks (DNNs) to image denoising has notably
challenged traditional denoising methods, particularly within complex noise
scenarios prevalent in medical imaging. Despite the effectiveness of
traditional and some DNN-based methods, their reliance on high-quality,
noiseless ground truth images limits their practical utility. In response to
this, our work introduces and benchmarks innovative unsupervised learning
strategies, notably Stein's Unbiased Risk Estimator (SURE), its extension
(eSURE), and our novel implementation, the Extended Poisson Unbiased Risk
Estimator (ePURE), within medical imaging frameworks.
  This paper presents a comprehensive evaluation of these methods on MRI data
afflicted with Gaussian and Poisson noise types, a scenario typical in medical
imaging but challenging for most denoising algorithms. Our main contribution
lies in the effective adaptation and implementation of the SURE, eSURE, and
particularly the ePURE frameworks for medical images, showcasing their
robustness and efficacy in environments where traditional noiseless ground
truth cannot be obtained.
