Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer (Englisch)
Freier Zugriff
- Neue Suche nach: Lian, Jian
- Neue Suche nach: Hou, Sujuan
- Neue Suche nach: Sui, Xiaodan
- Neue Suche nach: Xu, Fangzhou
- Neue Suche nach: Zheng, Yuanjie
- Neue Suche nach: Lian, Jian
- Neue Suche nach: Hou, Sujuan
- Neue Suche nach: Sui, Xiaodan
- Neue Suche nach: Xu, Fangzhou
- Neue Suche nach: Zheng, Yuanjie
In:
IET Computer Vision
;
12
, 6
;
900-907
;
2018
- Aufsatz (Zeitschrift) / Elektronische Ressource
-
Titel:Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer
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Beteiligte:Lian, Jian ( Autor:in ) / Hou, Sujuan ( Autor:in ) / Sui, Xiaodan ( Autor:in ) / Xu, Fangzhou ( Autor:in ) / Zheng, Yuanjie ( Autor:in )
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Erschienen in:IET Computer Vision ; 12, 6 ; 900-907
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Verlag:
- Neue Suche nach: The Institution of Engineering and Technology
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Erscheinungsdatum:21.05.2018
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Format / Umfang:8 pages
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:medical image processing , suitable degradation models , retinal OCT images , nonmodel architecture , deconvolution operators , learning (artificial intelligence) , optical coherence tomography systems , eye , neural nets , model-based solutions , image pre-processing tasks , parameter-free situations , current deblurring research , OCT deblurring methods , neural network architectures , convolution , retinal optical coherence tomography , optical tomography , degradation effects , state-of-the-art model , double convolution layer , biomedical optical imaging , deep learning strategy , image restoration , traditional model , deep convolutional neural network , deconvolution
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Datenquelle:
Metadata by IET is licensed under CC BY 3.0
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