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Single Image Super Resolution Techniques Based on Deep Learning: Status, Applications and Future Directions

Ying Liu 1,2, Yangge Qiao 1, Yu Hao 1,2, Fuping Wang 1,2, and Sheikh Faisal Rashid 3
1. Center for Image and Information Processing, Xi’an University of Posts and Telecommunications, Xi’an, China
2. Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, China
3. Artificial Intelligence Research Lab, Al-Khawarazimi Institute of Computer Science (KICS), Department of Computer Science, University of Engineering & Technology (UET), Main GT Road Lahore, Pakistan

Abstract—Single Image Super Resolution (SISR) reconstruction aims to recover high-resolution images from corresponding Low-Resolution (LR) versions, which is essentially an ill-posed inverse problem. In recent years, learning-based methods have been frequently exploited to tackle this problem, which correspond to promising calculation efficiency and performance, especially in image sharpening processing based on deep neural networks. Learning-based methods can be generally categorized as conventional methods and deep learning-based methods. This survey aims to review deep learning-based image super-resolution methods, including Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) based on internal network structure. Furthermore, this paper describes the applications of single-frame image super resolution in various practical fields. In addition, a few future research directions of image super resolution techniques are identified.

Index Terms—single image super-resolution reconstruction, deep learning, convolutional neural networks, generative adversarial networks

Cite: Ying Liu, Yangge Qiao, Yu Hao, Fuping Wang, and Sheikh Faisal Rashid, "Single Image Super Resolution Techniques Based on Deep Learning: Status, Applications and Future Directions," Journal of Image and Graphics, Vol. 9, No. 3, pp. 74-86, September 2021. doi: 10.18178/joig.9.3.74-86

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.