VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data

Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results than conventional two-dimensional (2D) processing. H...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 30; p. 1
Main Authors:	Li, Yinhao, Iwamoto, Yutaro, Lin, Lanfen, Xu, Rui, Chen, Yen-Wei
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3D Image Super-Resolution Accuracy Artificial neural networks Computational modeling Computed tomography Computer vision Convolution Feature extraction Feature maps Image reconstruction Image resolution Lightweight Lightweight Convolutional Neural Network Magnetic resonance imaging Mathematical models Medical diagnostic imaging Medical imaging Medical Volumetric Image Processing Modules Parameters Queues Solid modeling Three-dimensional displays Training
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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