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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  4. 10.5370/KIEE.2023.72.3.434
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References

1 
Y. C. Huang, Y. S. Tung, J. C. Chen, S. W. Wang, J. L. Wu, 2005, An adaptive edge detection based colorization algorithm and its applications, In Proceedings of the 13th annual ACM international conference on Multimedia, pp. 351-354DOI
2 
A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, S. Lin, 2011, Semantic colorization with internet images, in ACM Transactions on Graphics, Vol. 30, No. 6, pp. 156DOI
3 
R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, A. A. Efros, 2017, Real-time user-guided image colorization with learned deep priors, ACM Transactions on Graphics (TOG), Vol. 36, No. 4, pp. 1-11DOI
4 
S. Jheng-Wei, C. Hung-Kuo, H. Jia-Bin, 2020, Instance-aware image colorization, in IEEE conference on computer vision and pattern recognition, pp. 7968-7977Google Search
5 
Y. Wu, X. Wang, Y. Li, H. Zhang, X. Zhao, Y. Shan, 2021, Towards vivid and diverse image colorization with generative color prior, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14377-14386Google Search
6 
R. Timofte, V. De Smet, L. Van Gool, 2013, Anchored neighborhood regression for fast example-based super-resolution, In Proceedings of the IEEE international conference on computer vision, pp. 1920-1927Google Search
7 
K.I. Kim, Y. Kwon, 2010, Single-image super-resolution using sparse regression and natural image prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 6, pp. 1127-1133DOI
8 
C. Dong, C.C. Loy, K. He, X. Tang, 2014, Learning a deep convolutional network for image super-resolution., in European conference on computer vision, pp. 184-199DOI
9 
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, 2021, Swinir: Image restoration using swin transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1833-1844Google Search
10 
B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, 2017, Enhanced deep residual networks for single image super-resolution, in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 136-144Google Search
11 
G. Kim, K. Kang, S. Kim, H. Lee, S. Kim, J. Kim, S. Baek, S. Cho, 2022, BigColor: Colorization using a Generative Color Prior for Natural Images, In European Conference on Computer Vision, pp. 350-366Google Search
12 
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, S. Hochreiter, 2017, Gans trained by a two time-scale update rule converge to a local nash equilibrium, Advances in neural information processing systems, Vol. 30Google Search
13 
J. H. Lim, J. C. Ye, 2017, Geometric gan, arXiv preprint arXiv:1705.02894Google Search
14 
A. Brock, J. Donahue, K. Simonyan, 2018, Large scale GAN training for high fidelity natural image synthesis, arXiv preprint arXiv:1809.11096Google Search
15 
K. Simonyan, A. Zisserman, 2014, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409 1556Google Search
16 
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, 2016, Rethinking the inception architecture for computer vision, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826Google Search