|
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
|
| Volume 12 - Issue 13 |
| Published: May 2018 |
| Authors: Praneeth Sadda, Taha Qarni |
10.5120/ijais2018451755
|
Praneeth Sadda, Taha Qarni . Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems. 12, 13 (May 2018), 22-28. DOI=10.5120/ijais2018451755
@article{ 10.5120/ijais2018451755,
author = { Praneeth Sadda,Taha Qarni },
title = { Real-Time Medical Video Denoising with Deep Learning: Application to Angiography },
journal = { International Journal of Applied Information Systems },
year = { 2018 },
volume = { 12 },
number = { 13 },
pages = { 22-28 },
doi = { 10.5120/ijais2018451755 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A Praneeth Sadda
%A Taha Qarni
%T Real-Time Medical Video Denoising with Deep Learning: Application to Angiography%T
%J International Journal of Applied Information Systems
%V 12
%N 13
%P 22-28
%R 10.5120/ijais2018451755
%I Foundation of Computer Science (FCS), NY, USA
This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.