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Real-Time Medical Video Denoising with Deep Learning: Application to Angiography

Praneeth Sadda, Taha Qarni in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Praneeth Sadda, Taha Qarni
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  1. Praneeth Sadda and Taha Qarni. Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems 12(13):22-28, May 2018. URL, DOI BibTeX

    	author = "Praneeth Sadda and Taha Qarni",
    	title = "Real-Time Medical Video Denoising with Deep Learning: Application to Angiography",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2018",
    	volume = 12,
    	number = 13,
    	month = "May",
    	year = 2018,
    	issn = "2249-0868",
    	pages = "22-28",
    	url = "",
    	doi = "10.5120/ijais2018451755",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, 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.


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Angiography, deep learning, denoising, fluoroscopy, machine learning, neural network, real-time