Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning-Reference-Cited by-同舟云学术

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Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning

Published:2020 Issue: Volume:8 Page:67519-67529
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ma Yinjin^ORCID,Wei Biao^ORCID,Feng Peng^ORCID,He Peng^ORCID,Guo Xiaodong,Wang Ge

Funder

National Basic Research Program of China

Fundamental Research Funds for the Central Universities

Scientific and Technological Research Program of Chongqing Municipal Educational Commission

ICT NDT Engineering Research Center Fund of Chongqing University

Scientific Project of Chongqing Technology and Business Institute

National Institute of Biomedical Imaging and Bioengineering

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09058648.pdf?arnumber=9058648

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1. BMAD-Net: An attention mechanism network using block match for laboratory X-ray Microscopy denoising;Measurement;2025-01

2. A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective;Biomedical Engineering Letters;2024-08-30

3. Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework;Electronics;2024-08-12

4. Transformative Noise Reduction: Leveraging a Transformer-Based Deep Network for Medical Image Denoising;Mathematics;2024-07-24

5. Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images;Physica Scripta;2024-05-16

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