KIEE - The Transactions of the Korean Institute of Electrical Engineers

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D. H. Jeong, C. H. Son, 2021, Face superresolution in heavy rain conditions, Proceedings of KIIT Conference, pp. 426-430

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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Quio, C. C. Loy, 2018, Esrgan: Enhanced super-resolution generative adversarial networks, Proceedings of the European conference on computer vision workshops

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J. Li, Z. Pei, T. Zeng, 2021, From beginner to master: A survey for deep learning-based single-image super-resolution, arXiv preprint arXiv:2109.14335

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J. S. Yoon, T. H. Kim, Y. S. Choe, 2021, GAN based Single Image Super-Resolution via Spatially Adaptive De-normalization, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 2, pp. 402-407

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C. Dong, C. C. Loy, K. He, X. Tang, 2015, Image super-resolution using deep convolutional networks, IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No. 2, pp. 295-307

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J. W. Kim, J. K. Lee, K. M. Lee, 2016, Accurate image super-resolution using very deep convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1646-1654

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C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, 2017, Photo-realistic single image super-resolution using a generative adversarial network, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681-4690

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B. Lim, S. H. Son, H. W. Kim, S. J. Nah, K. M. Lee, 2017, Enhanced deep residual networks for single image super-resolution, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 136-144

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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, 2014, Generative adversarial nets, Advances in neural information processing systems

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M. S. Sajjadi, B. Scholkopf, M. Hirsch, 2017, Enhancenet: Single image super-resolution through automated texture synthesis, Proceedings of the IEEE international conference on computer vision, pp. 4491-4500

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S. J. Park, H. S. Son, S. H. Cho, K. S. Hong, S. Y. Lee, 2018, Srfeat: Single image super-resolution with feature discrimination, Proceedings of the European conference on computer vision, pp. 439-455

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Y. Yuan, S. Liu, J. Zhang, Y. Zhang, C. Dong, L. Lin, 2018, Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 701-710

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J. M. Ryu, J. M. Ma, 2021, A Study on Deep Learning-Based Super-Resolution Network Architecture for Military SAR Image, Korean Journal of Computational Design and Engineering, Vol. 26, No. 2, pp. 154-162

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Y. J. Choi, M. S. Kim, Y. W. Kim, S. H. Han, 2020, A Study of CNN-based Super-Resolution Method for Remote Sensing Image, Korean Journal of Remote Sensing, Vol. 36, No. 3, pp. 449-460

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H. S. Ha, B. Y. Hwang, 2018, Enhancement method of CCTV video quality based on SRGAN, Journal of Korea Multimedia Society, Vol. 21, No. 9, pp. 1027-1034

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Oh-Seol Kwon, 2020, Real-time low-resolution face recognition algorithm for surveillance systems, Journal of Broadcast Engineering, Vol. 25, No. 1, pp. 105-108

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J. M. Choi, D. J. Kang, 2017, Deep Super-resolution Method via Generative Adversarial Networks for License Place Image Enhancement, Journal of Institute of Control, Robotics and Systems, Vol. 23, No. 8, pp. 635-643

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S. J. Lee, T. J. Kim, C. H. Lee, S. B. Yoo, 2021, Image Super-Resolution for Improving Object Recognition Accuracy, Journal of the Korea Institute of Information and Communication Engineering, Vol. 25, No. 6, pp. 774-784

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W. Wang, E. Xie, X. Liu, W. Wang, D. Liang, C. Shen, X. Bai, 2020, Scene text image super-resolution in the wild, European Conference on Computer Vision, Springer, Cham, pp. 650-666

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H. M. Choi, K. S. Seo, 2021, Reconstruction Error based Anomaly Detection Defects Using GAN, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 4, pp. 679-683

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K. H. Jeong, Ki. S. Seo, 2021, Abandonment Behavior Detection Using Ganerative Advesarial Networks, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 9, pp. 1331-1335

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J. H. Yang, T. G. Kim, S. G. Yoon, 2022, DCGAN based Event Detection Scheme Using D-PMU Data in Distribution Systems, The transactions of The Korean Institute of Electrical Engineers, Vol. 71, No. 4, pp. 555-565

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers