Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests

Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping...

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Bibliographic Details
Main Authors: Peijian Gu, Changhui Jiang, Min Ji, Qiyang Zhang, Yongshuai Ge, Dong Liang, Xin Liu, Yongfeng Yang, Hairong Zheng, Zhanli Hu
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:Sensors
Subjects:
coupled dictionary learning
low-dose CT
random forests
super-resolution
Online Access:http://www.mdpi.com/1424-8220/19/1/207

Internet

http://www.mdpi.com/1424-8220/19/1/207

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