Video Restoration using Convolution Neural Network

Video Restoration using Convolution Neural Network

Registration ID: IJNRD_180137

Published ID: IJNRD1807014

DOI: Click Here to Get

Sushant Deshmukh , Rajesh Patil

Neural Networks, Video Restoration

Neural networks have shown very promising results in a large number of research areas. With the introduction of convolution neural networks, they have been widely used in image processing. In this paper we implement Convolution Neural Network for video restoration. This is achieved by introducing higher frequency details using pre trained networks. Most of the research aims at improving video quality by increasing PSNR, but sometimes due to this the videos may become aesthetically less satisfying. While large image databases are available to train deep neural networks, it is more challenging to create a large video database of sufficient quality to train neural nets for video restoration. The dataset used for training the model is from DIV2K - bicubic downscaling x4 competition.Video restoration remains a challenging problem despite being a very active area of research. Even with huge strides made with single-image super-resolution, multi-frame techniques, which utilize multiple frames in improving the quality of a given frame, we have yet to fully take advantage of the power of deep learning.

Published Paper   E-Certificate

"Video Restoration using Convolution Neural Network", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.3, Issue 7, page no.78-80, July-2018, Available :https://ijnrd.org/papers/IJNRD1807014.pdf

Volume 3 Issue 7, July-2018

Pages : 78-80

Paper Reg. ID: IJNRD_180137

Published Paper Id: IJNRD1807014

Downloads: 000121118

Research Area: Engineering

Country: Thane, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD1807014.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD1807014

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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