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Improving Low-Dose CT Image Using Residual Convolutional Network

  • Published:2017 Issue: Volume:5 Page:24698-24705
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Yang Wei,Zhang Huijuan,Yang JianORCID,Wu JiasongORCID,Yin Xiangrui,Chen YangORCID,Shu Huazhong,Luo Limin,Coatrieux Gouenou,Gui Zhiguo,Feng Qianjin

Funder

State’s Key Project of Research and Development Plan

National Natural Science Foundation

Fundamental Research Funds for the Central Universities

Qing Lan Project in Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 87 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior;Computer Methods and Programs in Biomedicine;2024-02

2. An adaptive network model-based weighted similarity measure for CT image denoising;Soft Computing;2023-11-21

3. A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise;Radiological Physics and Technology;2023-11-13

4. An Optimized Learning Model for Image Denoising using Modern Deep Learning Approaches;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

5. Low Dose CT Image Reconstruction Using Deep Convolutional Residual Learning Network;SN Computer Science;2023-09-23
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