In this paper, a low-dose CT denoising method based on L1/L2regularization method of Markov chain Monte Carlo is studied. Firstly, the mathematical model and regularization method of low-dose CT denoising are summariz...In this paper, a low-dose CT denoising method based on L1/L2regularization 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 L1, L2, and L1/L2regularization terms in MCMC sampling at Gaussian noise levels. The experimental results show that L1/L2regularization has the best performance in balancing noise removal and image detail retention, significantly superior to single L1and L2regularization, which proves its effectiveness for low-dose CT denoising.展开更多
文摘In this paper, a low-dose CT denoising method based on L1/L2regularization 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 L1, L2, and L1/L2regularization terms in MCMC sampling at Gaussian noise levels. The experimental results show that L1/L2regularization has the best performance in balancing noise removal and image detail retention, significantly superior to single L1and L2regularization, which proves its effectiveness for low-dose CT denoising.
