IEEE Trans Med Imaging. 2023 Jun;42(6):1590-1602. doi: 10.1109/TMI.2022.3231428. Epub 2023 Jun 1.
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.
图像去噪是许多领域下游任务的前提。低剂量和光子计数计算机断层扫描（CT）去噪可以在最小化辐射剂量的情况下优化诊断性能。有监督的深度去噪方法很流行，但需要配对的干净或嘈杂的样本，而这些样本在实际中往往不可用。受独立噪声假设的限制，当前的自监督去噪方法无法处理 CT 图像中的相关噪声。在这里，我们提出了一种开创性的基于相似性的自监督深度去噪方法，称为 Noise2Sim，它以非局部和非线性的方式工作，不仅可以抑制独立噪声，还可以抑制相关噪声。从理论上讲，在温和的条件下，Noise2Sim 与有监督学习方法在渐近意义上是等价的。在实验中，在实际数据集上，Noise2Sim 从嘈杂的低剂量 CT 和光子计数 CT 图像中有效地恢复了内在特征，在视觉、定量和统计上与有监督学习方法一样有效，甚至更好。Noise2Sim 是一种通用的自监督去噪方法，在各种应用中具有很大的潜力。
