Robust Video Super-Resolution with Learned Temporal Dynamics

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 2526-2534

  Liu, Ding ^a   Wang, Zhaowen ^b   Fan, Yuchen ^a   Liu, Xianming ^c   Wang, Zhangyang ^d   Chang, Shiyu ^e   Huang, Thomas ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b ADOBE RESEARCH   (United States)

^c FACEBOOK   (United States)

^d TEXAS A AND M UNIVERSITY   (United States)

^e IBM RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALIGNMENT; COMPUTER VISION; OPTICAL RESOLVING POWER; SIGNAL RECEIVERS;

ALIGNMENT METHODS; SPATIAL ALIGNMENT; SPATIAL RELATIONS; TEMPORAL ADAPTIVE; TEMPORAL DYNAMICS; TEMPORAL INFORMATION; TEMPORAL RELATION; VIDEO SUPER-RESOLUTION;

COMPLEX NETWORKS;

EID: 85041903214     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.274     Document Type: Conference Paper

References (31)

1. http://ultravideo. cs. tut. fi/.
2. S. P. Belekos, N. P. Galatsanos, and A. K. Katsaggelos. Maximum a posteriori video super-resolution using a new multichannel image prior. TIP, 19 (6):1451-1464, 2010.
3. D. Dai, Y. Wang, Y. Chen, and L. Van Gool. Is image superresolution helpful for other vision tasks? In WACV, pages 1-9. IEEE, 2016.
4. C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV, pages 184-199. Springer, 2014.
5. C. Dong, C. C. Loy, K. He, and X. Tang. Image superresolution using deep convolutional networks. TPAMI, 38 (2):295-307, 2016.
6. C. Dong, C. C. Loy, and X. Tang. Accelerating the superresolution convolutional neural network. In ECCV, pages 391-407. Springer, 2016.
7. M. Dorr, T. Martinetz, K. R. Gegenfurtner, and E. Barth. Variability of eye movements when viewing dynamic natural scenes. Journal of vision, 10 (10):28-28, 2010.
8. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar. Fast and robust multiframe super resolution. TIP, 13 (10):1327-1344, 2004.
9. Y. Huang, W. Wang, and L. Wang. Bidirectional recurrent convolutional networks for multi-frame super-resolution. In NIPS, pages 235-243, 2015.
10. M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformer networks. In NIPS, pages 2017-2025, 2015.
11. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia, pages 675-678. ACM, 2014.
12. A. Kappeler, S. Yoo, Q. Dai, and A. K. Katsaggelos. Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging, 2 (2):109-122, 2016.
13. C. Keimel, J. Habigt, T. Habigt, M. Rothbucher, and K. Diepold. Visual quality of current coding technologies at high definition iptv bitrates. In Multimedia Signal Processing (MMSP), 2010 IEEE International Workshop on, pages 390-393. IEEE, 2010.
14. J. Kim, J. K. Lee, and K. M. Lee. Accurate image superresolution using very deep convolutional networks. In CVPR, 2016.
15. J. Kim, J. K. Lee, and K. M. Lee. Deeply-recursive convolutional network for image super-resolution. In CVPR, 2016.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.
17. R. Liao, X. Tao, R. Li, Z. Ma, and J. Jia. Video superresolution via deep draft-ensemble learning. In CVPR, pages 531-539, 2015.
18. J. Y. Lin, R. Song, C.-H. Wu, T. Liu, H. Wang, and C.-C. J. Kuo. Mcl-v: A streaming video quality assessment database. Journal of Visual Communication and Image Representation, 30:1-9, 2015.
19. C. Liu. Beyond pixels: exploring new representations and applications for motion analysis. PhD thesis, Citeseer, 2009.
20. C. Liu and D. Sun. On Bayesian adaptive video super resolution. TPAMI, 36 (2):346-360, 2014.
21. D. Liu, Z. Wang, N. Nasrabadi, and T. Huang. Learning a mixture of deep networks for single image super-resolution. In ACCV, pages 145-156. Springer, 2016.
22. D. Liu, Z. Wang, B. Wen, J. Yang, W. Han, and T. S. Huang. Robust single image super-resolution via deep networks with sparse prior. TIP, 25 (7):3194-3207, 2016.
23. Z. Ma, R. Liao, X. Tao, L. Xu, J. Jia, and E. Wu. Handling motion blur in multi-frame super-resolution. In CVPR, pages 5224-5232, 2015.
24. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, pages 807-814, 2010.
25. K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack. Study of subjective and objective quality assessment of video. TIP, 19 (6):1427-1441, 2010.
26. W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In CVPR, pages 1874-1883, 2016.
27. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, pages 1-9, 2015.
28. H. Takeda, P. Milanfar, M. Protter, and M. Elad. Superresolution without explicit subpixel motion estimation. TIP, 18 (9):1958-1975, 2009.
29. Z. Wang, S. Chang, Y. Yang, D. Liu, and T. S. Huang. Studying very low resolution recognition using deep networks. In CVPR, pages 4792-4800, 2016.
30. Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang. Deep networks for image super-resolution with sparse prior. In ICCV, pages 370-378, 2015.
31. L. Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In CVPR, pages 529-534. IEEE, 2011.