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This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks.
Wang, Xintao, et al. ”Esrgan: Enhanced super-resolution generative adversarial networks.” Proceedings of the European conference on computer vision (ECCV) workshops. 2018
2018
Earlier work this paper cites.
Gopan, Karthika, and G. S. Kumar. ”Video super resolution with generative adversarial network.” 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018
2018
Earlier work this paper cites.
Thawakar, Omkar, et al. ”Image and video super resolution using recurrent generative adversarial network.” 2019 16th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, 2019
2019
Earlier work this paper cites.
Ou, Chaojie, and Fakhri Karray. ”Enhancing driver distraction recognition using generative adversarial networks.” IEEE Transactions on Intelligent Vehicles 5.3 (2019): 385-396
2019
Earlier work this paper cites.
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F. and Zheng, Y., 2019. Recent progress on generative adversarial networks (GANs): A survey. IEEE access, 7, pp.36322-36333
2019
Earlier work this paper cites.
Purohit, Kuldeep, Srimanta Mandal, and A. N. Rajagopalan. ”Mixed-dense connection networks for image and video super-resolution.” Neurocomputing 398 (2020): 360-376
2020
Earlier work this paper cites.
Yang, Zhuoyuan, Ping Shi, and Da Pan. ”A survey of super-resolution based on deep learning.” 2020 International Conference on Culture-oriented Science & Technology (ICCST). IEEE, 2020
2020
Cited alongside, same era.
Yang, Bin, et al. ”Super-resolution generative adversarial networks based on attention model.” 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020
2020
Cited alongside, same era.
Wang, Ting, et al. ”Visual perception enhancement for HEVC compressed video using a generative adversarial network.” 2020 International Conference on UK-China Emerging Technologies (UCET). IEEE, 2020
2020
Cited alongside, same era.
Jia, Rongzhao, and Xiaohong Wang. ”Research on super-resolution reconstruction algorithm of image based on generative adversarial network.” Journal of Physics: Conference Series. Vol. 1944. No. 1. IOP Publishing, 2021
2021
Cited alongside, same era.
Molefe, Molefe, and Richard Klein. ”Image Super-Resolution Using Generative Adversarial Networks with Learned Degradation Operators.” MATEC Web of Conferences. Vol. 370. EDP Sciences, 2022
2022
Later among the works it cites.
Xiang, Nan, Bin Tang, and Lu Wang. ”Image Super-Resolution Method Based on Improved Generative Adversarial Network.” 2022 IEEE 5th International Conference on Electronics Technology (ICET). IEEE, 2022
2022
Later among the works it cites.
Reddy, K. Srinivasa, et al. ”Implementation of super resolution in images based on generative Adversarial network.” 2022 8th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
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Wang, J., Teng, G. and An, P., 2021. Video super-resolution based on generative adversarial network and edge enhancement. Electronics, 10(4), p.459
2021
Cited alongside, same era.
Wen, Weilei, et al. ”Video super-resolution via a spatio-temporal alignment network.” IEEE Transactions on Image Processing 31 (2022): 1761-1773
2022
Later among the works it cites.