| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03044 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203044 | |
| Published online | 29 July 2020 | |
Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture
1 Ramrao Adik Institute of Technology
2 Department of Computer Engineering
3 Mumbai, India
* e-mail: vanitamane1@gmail.com
** e-mail: suchit966@gmail.com
*** e-mail: praneya0028@gmail.com
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.
Key words: MRI / Super Resolution / SRCNN / FSRCNN
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.