@Article{JCM-42-4,
author = {Lu, Jian and Huaxuan, Hu and Zou, Yuru and Zhaosong, Lu and Xiaoxia, Liu and Zu, Keke and Lin, Li},
title = {A Nonlocal Kronecker-Basis-Representation Method for Low-Dose CT Sinogram Recovery},
journal = {Journal of Computational Mathematics},
year = {2024},
volume = {42},
number = {4},
pages = {1080--1108},
abstract = {<p style="text-align: justify;">Low-dose computed tomography (LDCT) contains the mixed noise of Poisson and Gaussian, which makes the image reconstruction a challenging task. In order to describe the
statistical characteristics of the mixed noise, we adopt the sinogram preprocessing as a standard maximum a posteriori (MAP). Based on the fact that the sinogram of LDCT has nonlocal self-similarity property, it exhibits low-rank characteristics. The conventional way of
solving the low-rank problem is implemented in matrix forms, and ignores the correlations
among similar patch groups. To avoid this issue, we make use of a nonlocal Kronecker-Basis-Representation (KBR) method to depict the low-rank problem. A new denoising
model, which consists of the sinogram preprocessing for data fidelity and the nonlocal
KBR term, is developed in this work. The proposed denoising model can better illustrate
the generative mechanism of the mixed noise and the prior knowledge of the LDCT. Numerical results show that the proposed denoising model outperforms the state-of-the-art
algorithms in terms of peak-signal-to-noise ratio (PSNR), feature similarity (FSIM), and
normalized mean square error (NMSE).</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.2301-m2022-0091},
url = {https://global-sci.com/article/91089/a-nonlocal-kronecker-basis-representation-method-for-low-dose-ct-sinogram-recovery}
}