Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The...

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Bibliographic Details
Main Authors: Mane Vanita, Jadhav Suchit, Lal Praneya
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:ITM Web of Conferences
Subjects:
mri
super resolution
srcnn
fsrcnn
Online Access:https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03044.pdf
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spelling doaj-b4f0f7355ecb4525a888ede48408c56a2021-04-02T12:37:22ZengEDP SciencesITM Web of Conferences2271-20972020-01-01320304410.1051/itmconf/20203203044itmconf_icacc2020_03044Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architectureMane Vanita0Jadhav Suchit1Lal Praneya2Ramrao Adik Institute of TechnologyDepartment of Computer EngineeringMumbaiSingle image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03044.pdfmrisuper resolutionsrcnnfsrcnn
collection DOAJ
language English
format Article
sources DOAJ
author Mane Vanita
Jadhav Suchit
Lal Praneya
spellingShingle Mane Vanita
Jadhav Suchit
Lal Praneya
Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
ITM Web of Conferences
mri
super resolution
srcnn
fsrcnn
author_facet Mane Vanita
Jadhav Suchit
Lal Praneya
author_sort Mane Vanita
title Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
title_short Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
title_full Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
title_fullStr Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
title_full_unstemmed Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
title_sort image super-resolution for mri images using 3d faster super-resolution convolutional neural network architecture
publisher EDP Sciences
series ITM Web of Conferences
issn 2271-2097
publishDate 2020-01-01
description Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.
topic mri
super resolution
srcnn
fsrcnn
url https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03044.pdf
work_keys_str_mv AT manevanita imagesuperresolutionformriimagesusing3dfastersuperresolutionconvolutionalneuralnetworkarchitecture
AT jadhavsuchit imagesuperresolutionformriimagesusing3dfastersuperresolutionconvolutionalneuralnetworkarchitecture
AT lalpraneya imagesuperresolutionformriimagesusing3dfastersuperresolutionconvolutionalneuralnetworkarchitecture
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