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.

翻译：深度神经网络（DNN）在图像去噪中的应用显著挑战了传统去噪方法，尤其是在医学成像中普遍存在的复杂噪声场景下。尽管传统方法和一些基于DNN的方法具有有效性，但它们对高质量、无噪声真实图像的依赖限制了其实用性。针对这一问题，我们的工作引入并基准测试了创新的无监督学习策略，特别是斯坦因无偏风险估计器（SURE）、其扩展形式（eSURE）以及我们新颖的实现——扩展泊松无偏风险估计器（ePURE），并将其置于医学成像框架内。本文对这些方法在受高斯噪声和泊松噪声影响的MRI数据上进行了全面评估，这是医学成像中的典型场景，但对大多数去噪算法具有挑战性。我们的主要贡献在于有效适配并实现了SURE、eSURE，特别是ePURE框架用于医学图像，展示了它们在无法获取传统无噪声真实图像的环境中的鲁棒性和有效性。