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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

has been cited by the following article:

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

  AUTHORS: Shuoqi Yu

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

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.2, February 17, 2025

  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.