全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Markov Chain Monte Carlo-Based L1/L2 Regularization and Its Applications in Low-Dose CT Denoising

DOI: 10.4236/jamp.2025.132021, PP. 419-428

Keywords: Low-Dose CT Denoising, Regularization, Statistical Inverse Problem, MCMC Sampling

Full-Text   Cite this paper   Add to My Lib

Abstract:

In this paper, a low-dose CT denoising method based on L 1 / L 2 regularization method of Markov chain Monte Carlo is studied. Firstly, the mathematical model and regularization method of low-dose CT denoising are summarized, and then the theoretical basis of MCMC method and its application in image denoising are introduced. We evaluated the performance of various regularization strategies by comparing the denoising effects of L 1 , L 2 , and L 1 / L 2 regularization terms in MCMC sampling at Gaussian noise levels. The experimental results show that L 1 / L 2 regularization has the best performance in balancing noise removal and image detail retention, significantly superior to single L 1 and L 2 regularization, which proves its effectiveness for low-dose CT denoising.

References

[1]  Mayo, J.R., Hartman, T.E., Lee, K.S., Primack, S.L., Vedal, S. and Müller, N.L. (1995) CT of the Chest: Minimal Tube Current Required for Good Image Quality with the Least Radiation Dose. American Journal of Roentgenology, 164, 603-607.
https://doi.org/10.2214/ajr.164.3.7863879
[2]  Haque, A., Wang, A.S. and Imran, A. (2022) Noise2Quality: Non-Reference, Pixel-Wise Assessment of Low Dose CT Image Quality. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, San Diego, 20-24 February 2022, 120351C.
https://doi.org/10.1117/12.2611254
[3]  Hadamard, J. (1923) Lectures on Cauchy’s Problem in Linear Partial Differential Equations. Yale University Press.
[4]  Bertero, M. and Boccacci, P. (1998) Introduction to Inverse Problems in Imaging. IOP Publishing Ltd.
https://doi.org/10.1887/0750304359
[5]  Liu, J., Huang, J. and Zhang, S. (2012) Limited-Angle CT Reconstruction via the Minimization. Inverse Problems and Imaging, 6, 357-375.
[6]  Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953) Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics, 21, 1087-1092.
https://doi.org/10.1063/1.1699114
[7]  Roberts, G.O., Gelman, A. and Gilks, W.R. (1997) Optimal Scaling for Various Metropolis-Hastings Algorithms. Statistical Science, 7, 473-483.
[8]  Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004) Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13, 600-612.
https://doi.org/10.1109/tip.2003.819861
[9]  Li, T., Li, X., Wang, J., et al. (2004) Nonlinear Sinogram Smoothing for Low-Dose X-Ray CT. IEEE Transactions on Nuclear Science, 51, 2505-2513.
https://doi.org/10.1109/tns.2004.834824

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133