2024-01-02
2024-03-22
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.