Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,which makes the image reconstruction a challenging task.In order to describe the statistical characteristics of the mixed noise,we ad...Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,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 stan-dard maximum a posteriori(MAP).Based on the fact that the sinogram of LDCT has non-local 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.Nu-merical 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).展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U21A20455,61972265,11871348)by the Natural Science Foundation of Guangdong Province of China(Grant No.2020B1515310008)+3 种基金by the Department of Education of Guangdong Province of China(Grant No.2019KZDZX1007)by the PolyU internal Grant No.P0040271by the Pazhou Laboratory,Guangzhou,China(Grant No.PZL2021KF0017)by the Guangdong Key Laboratory of Intelligent Information Processing,China.
文摘Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,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 stan-dard maximum a posteriori(MAP).Based on the fact that the sinogram of LDCT has non-local 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.Nu-merical 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).
