An Overview on Deep Learning in Image Super-Resolution for Advanced Machine Vision System

DOI

10.2991/ahis.k.210913.027How to use a DOI?

Keywords

Deep learning, Image processing, Machine-learning, Methods, Super-resolution

Abstract

Image spatial resolution means the ability of the sensor to measure the smallest pixel size object. It focuses on recovering a less resolution (LR) image to high resolution (HR) image observations. Due to the exceptional research and application realizations of machine learning at home and abroad, the implementation effect of machine learning algorithms in image super resolution will be enormous. For this, deep learning has become a powerful learning tool for computer vision works. Furthermore, the performance of image super-resolution methods is showing significantly improved by using deep learning. In this paper, the basic image super-resolution methods based on deep learning have been discussed in detail along with the latest applications using super-resolution techniques. In addition, the current open issues and challenges for future research work are discussed. Finally, the main application areas of image super-resolution based on deep learning domain are presented.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Meet Kumari
PY  - 2021
DA  - 2021/09/13
TI  - An Overview on Deep Learning in Image Super-Resolution for Advanced Machine Vision System
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 218
EP  - 225
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.027
DO  - 10.2991/ahis.k.210913.027
ID  - Kumari2021
ER  -