ARCHIVES

[1] T. Welsh, M. Ashikhmin, and K. Mueller, “Transferring color to grayscale image,” ACM Transactional on Graphics, vol. 21, no. 3,
pp. 277-280, 2002.
[2] R. Irony, D. Cohen-Or, and D. Lischinski, “Colorization by example,” in EurographicsSymp. on Rendering, vol. 2. Citeseer, 2005.
[3] X. Liu, L. Wan, Y. Qu, T.-T. Wong, S. Lin, C.-S. Leung, and P.-A. Heng, “Intrinsic colorization,” in TOG, vol. 27, no. 5. ACM, 2008,
p. 152.
[4] Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” in TOG,
vol. 30, no. 6. ACM, 2011, p. 156.
[5] Z. Cheng, Q. Yang, and B. Sheng, “Deep colorization,” In Proceedings of the IEEE International Conference on Computer Vision
(ICCV), pp. 415– 423, 2015.
[6] G. Larsson, M. Maire, and G. Shakhnarovich, “Learning representations for automatic colorization,” In Proceedings of the
European Conference on Computer Vision (ECCV), pp. 577–593, 2016.
[7] R. Zhang, P. Isola, and A.A. Efros, “Colorful image colorization,” In Proceedings of European Conference on Computer Vision
(ECCV), pp. 649–666, 2016
[8] S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for
Automatic Image Colorization with Simultaneous Classification,” ACM Transactions on Graphics, vol. 35, no. 4, pp. 110:1-110:11,
2016.
[9] R. Zhang, J. Zhu, P. Isola, X. Geng, A.S. Lin, T. Yu, and A.A. Efros, “Real-time user-guided image colorization with learned deep
priors,” ACM Transactions n Graphics, vol. 36, no. 4, pp. 119:1-119:11, 2017.
[10] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,”
in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026–1034.
[11] Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in
neural information processing systems, 2012, pp. 1097–1105.
[12] W. Ouyang and X. Wang, “Joint deep learning for pedestrian detection,” in ICCV. IEEE, 2013, pp. 2056–2063.
[13] C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in ECCV. Springer,
2014, pp. 184–199.
[14] Z. Yan, H. Zhang, B. Wang, S. Paris, and Y. Yu, “Automatic photo adjustment using deep neural networks,” ACM Trans. Graph.,
vol. 35, no. 2, pp. 11:1–11:15, Feb. 2016.