The following publications are possibly variants of this publication:
- DD-DCSR: Image Denoising for Low-Dose CT via Dual-Dictionary Deep Convolutional Sparse RepresentationShu Li, Yi Liu 0007, Rongbiao Yan, Haowen Zhang, Shubin Wang, Ting Ding, Zhiguo Gui. tci, 10:899-914, 2024. [doi]
- Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder NetworkYanqin Kang, Jin Liu, Tao Liu, Jun-qiang. bmei 2022: 1-6 [doi]
- Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic modelQiwei Li, Chen Li, Chenggong Yan, Xiaomei Li, Haixia Li, Tianjing Zhang, Hui Song, Roman Schaffert, Weimin Yu, Yu Fan, Jianwei Ye, Hao Chen. cyberc 2022: 198-205 [doi]
- PFCM: Poisson Flow Consistency Models for Low-Dose CT Image DenoisingDennis Hein, Grant M. Stevens, Adam S. Wang, Ge Wang 0001. tmi, 44(7):2989-3001, July 2025. [doi]
- A Novel Total Variation Model for Low-Dose CT Image DenoisingWenbin Chen, Yanling Shao, Yanling Wang, Quan Zhang, Yi Liu 0007, Linhong Yao, Yan Chen, Guanru Yang, Zhiguo Gui. access, 6:78892-78903, 2018. [doi]
- A novel denoising method for low-dose CT images based on transformer and CNNJu Zhang, Zhibo Shangguan, Weiwei Gong, Yun Cheng. cbm, 163:107162, September 2023. [doi]
- Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture DecompositionHao Chen, Yi Liu, Pengcheng Zhang, Jiaqi Kang, Zhiyuan Li, Weiting Cheng, Zhiguo Gui. cssp, 43(5):3073-3101, May 2024. [doi]