降噪
深度学习
人工智能
计算机科学
图像去噪
图像质量
噪音(视频)
模式识别(心理学)
计算机视觉
图像(数学)
作者
Fan Zhang,Jingyu Liu,Ying Liu,Xinhong Zhang
出处
期刊:Radiation Protection Dosimetry
[Oxford University Press]
日期:2023-01-02
卷期号:199 (4): 337-346
被引量:4
摘要
Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences between the traditional methods and the deep learning-based methods. This paper discusses the denoising approaches of low-dose CT image via deep learning. Deep learning-based methods have achieved relatively ideal denoising effects in both subjective visual quality and quantitative objective metrics. This paper focuses on three state-of-the-art deep learning-based image denoising methods, in addition, four traditional methods are used as the control group to compare the denoising effect. Comprehensive experiments show that the deep learning-based methods are superior to the traditional methods in low-dose CT images denoising.
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