The following publications are possibly variants of this publication:
- SwinCT: feature enhancement based low-dose CT images denoising with swin transformerMuwei Jian, Xiaoyang Yu, Haoran Zhang, Chengdong Yang. mms, 30(1):1, February 2024. [doi]
- Low Dose CT Image Denoising Using Efficient Transformer with SimpleGate MechanismLianjin Xiong, Wei Qiu, Ning Li, Yishi Li, Yangsong Zhang. iconip 2023: 556-566 [doi]
- Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image DenoisingHuan Wang, Jianning Chi, Chengdong Wu, Xiaosheng Yu, Hao Wu 0064. jdi, 36(4):1894-1909, August 2023. [doi]
- Low-Dose CT Denoising via Sinogram Inner-Structure TransformerLiutao Yang, Zhongnian Li, Rongjun Ge, Junyong Zhao, Haipeng Si, Daoqiang Zhang. tmi, 42(4):910-921, April 2023. [doi]
- TED-Net: Convolution-Free T2T Vision Transformer-Based Encoder-Decoder Dilation Network for Low-Dose CT DenoisingDayang Wang, Zhan Wu, Hengyong Yu. miccai 2021: 416-425 [doi]
- Two stage residual CNN for texture denoising and structure enhancement on low dose CT imageLiangliang Huang, Huiyan Jiang, Shaojie Li, Zhiqi Bai, Jitong Zhang. cmpb, 184:105115, 2020. [doi]
- Enhance Generative Adversarial Networks By Wavelet Transform To Denoise Low-Dose Ct ImagesWanqi Su, Yili Qu, Chufu Deng, Ying Wang, Fudan Zheng, Zhiguang Chen. icip 2020: 350-354 [doi]
- Masked Autoencoders for Low-dose CT DenoisingDayang Wang, Yongshun Xu, Shuo Han 0009, Hengyong Yu. isbi 2023: 1-4 [doi]