Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.

低剂量计算机断层扫描（CT）成像是最常用的医学成像方式。尽管剂量的减少会降低辐射带来的风险，但会导致噪声水平的提高。因此，强制性要求将降噪技术作为预处理和/或后处理步骤，以更好地进行疾病诊断。核规范的最小化在当代引起了很多研究兴趣。本文提出了一种基于低秩近似的CT图像去噪方法，它可以有效地利用全局空间相关性和局部平滑特性。张量核范数用于描述全局特性，张量总变化量用于表征局部平滑度并提高全局平滑度。由此产生的优化问题通过乘数的交替方向方法（ADMM）技术得以解决。在模拟和真实CT数据上的实验结果证明，所提出的方法优于最新技术。