SureUnet: sparse autorepresentation encoder U-Net for noise artifact suppression in low-dose CT

Low-dose computed tomography (LDCT) is desirable due to ionizing radiation, but the resulting images suffer from serious streak artifacts and spot noise. Recently, deep learning (DL)-based methods have emerged as promising alternatives for medical image processing. However, most DL-based methods are...

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Vydané v:	Neural computing & applications Ročník 37; číslo 11; s. 7561 - 7573
Hlavní autori:	Liu, Jin, Zhang, Tingyu, Kang, Yanqin, Qiang, Jun, Hu, Dianlin, Zhang, Yikun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.04.2025 Springer Nature B.V
Predmet:	Artificial Intelligence Coders Computational Biology/Bioinformatics Computational Science and Engineering Computed tomography Computer Science Data Mining and Knowledge Discovery Datasets Decoding Image processing Image Processing and Computer Vision Image quality Ionizing radiation Medical imaging Multilayers Noise reduction Probability and Statistics in Computer Science S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing Special Issue on Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing Noise artifacts Sparse autorepresentation Low-dose CT U-Net
ISSN:	0941-0643, 1433-3058
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