[1] [1] ZHAO T,HOFFMAN J,MCNITT-GRAY M,et al.Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics[J].Medical Physics,2019,46(1):190-198.

[2] [2] KIM B G,KANG S H,PARK C R,et al.Noise level and similarity analysis for computed tomographic thoracic image with fast non-local means denoising algorithm[J].Applied Sciences,2020,10(21):7455.

[3] [3] DEEBA F,KUN S,DHAREJO F A,et al.Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm[J].IET Image Processing,2020,14(11):2365-2375.

[4] [4] CHEN Z,ZHANG Q,ZHOU C,et al.Low-dose CT reconstruction method based on prior information of normal-dose image[J].Journal of X-ray Science and Technology,2020,28(6):1091-1111.

[5] [5] CHEN H,ZHANG Y,KALRA M K,et al.Low-dose CT with a residual encoder-decoder convolutional neural network[J].IEEE Transactions on Medical Imaging,2017,36(12):2524-2535.

[6] [6] KANG E,CHANG W,YOO J,et al.Deep convolutional framelet denosing for low-dose CT via wavelet residual network[J].IEEE Transactions on Medical Imaging,2018,37(6):1358-1369.

[7] [7] SHI Z,LI H,CAO Q,et al.A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks[J].Medical Physics,2021,48(6):2891-2905.

[8] [8] GU J,YE J C.AdaIN-based tunable CycleGAN for efficient unsupervised low-dose CT denoising[J].IEEE Transactions on Computational Imaging,2021,7:73-85.

[9] [9] LEE H Y,TSENG H Y,MAO Q,et al.DRIT++:Diverse image-to-image translation via disentangled representations[J].International Journal of Computer Vision,2020,128(10):2402-2417.

[10] [10] LEHTINEN J,MUNKBERG J,HASSELGREN J,et al.Noise2Noise:Learning image restoration without clean data［C］//International Conference on Machine Learning,July 10-15,2018,Stockholm,Sweden.New York:PMLR,2018,80:2917-2980.

[11] [11] PRAKASH M,LALIT M,TOMANCAK P,et al.Fully unsupervised probabilistic noise2void［C］//2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI),April 3-7,2020,Lowa City,IA,USA.New York:IEEE,2020:154-158.

[12] [12] BATSON J,ROYER L.Noise2Self:Blind denoising by self-supervision［C］//International Conference on Machine Learning,June 9-15,2019,Long Beach,California,USA.New York:PMLR,2019:524-533.

[13] [13] NIU C,FAN F,WU W,et al.Suppression of independent and correlated noise with similarity-based unsupervised deep learning［EB/OL］.(2021-09-30)［2023-01-04］.https://arxiv.org/abs/2011.03384v5.

[14] [14] RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolutional networks for biomedical image segmentation［C］//18th International Conference on Medical Image Computing and Computer-Assisted Intervention,October 5-9,2015,Munich,Germany.Berlin:Springer,2015:234-241.

[15] [15] ZHU E R,ZHAO H C,HU X F.Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning[J].Optoelectronics Letters,2022,18(9):547-552.

[16] [16] YANG L,SHANGGUAN H,ZHANG X,et al.High-frequency sensitive generative adversarial network for low-dose CT image denoising[J].IEEE Access,2019,8:930-943.

[17] [17] LI M,HSU W,XIE X,et al.SACNN:Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network[J].IEEE Transactions on Medical Imaging,2020,39(7):2289-2301.