Year: 2024
Author: Jian Lu, Huaxuan Hu, Yuru Zou, Zhaosong Lu, Xiaoxia Liu, Keke Zu, Lin Li
Journal of Computational Mathematics, Vol. 42 (2024), Iss. 4 : pp. 1080–1108
Abstract
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).
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2301-m2022-0091
Journal of Computational Mathematics, Vol. 42 (2024), Iss. 4 : pp. 1080–1108
Published online: 2024-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 29
Keywords: Low-dose computed tomography Kronecker-basis-representation Low-rank approximation Noise-generating-mechanism.
Author Details
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3D Poissonian image deblurring via patch-based tensor logarithmic Schatten-p minimization
Lu, Jian
Huang, Lin
Liu, Xiaoxia
Xie, Ning
Jiang, Qingtang
Zou, Yuru
Inverse Problems, Vol. 40 (2024), Iss. 6 P.065010
https://doi.org/10.1088/1361-6420/ad40c9 [Citations: 0]