References(50)
[1]
C. Dong,; C. C. Loy,; K. M. He,; X. O. Tang, Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 38, No. 2, 295-307, 2016.
[2]
M. Haris,; G. Shakhnarovich,; N. Ukita, Deep back-projection networks for super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1664-1673, 2018.
[3]
M. Haris,; M. R. Widyanto,; H. Nobuhara, Inception learning super-resolution. Applied Optics Vol. 56, No. 22, 6043, 2017.
[4]
J. Kim,; J. K. Lee,; K. M. Lee, Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1646-1654, 2016.
[5]
E. Faramarzi,; D. Rajan,; M. P. Christensen, Unified blind method for multi-image super-resolution and single/multi-image blur deconvolution. IEEE Transactions on Image Processing Vol. 22, No. 6, 2101-2114, 2013.
[6]
D. C. Garcia,; C. Dorea,; R. L. de Queiroz, Super resolution for multiview images using depth information. IEEE Transactions on Circuits and Systems for Video Technology Vol. 22, No. 9, 1249-1256, 2012.
[7]
J. Caballero,; C. Ledig,; A. Aitken,; A. Acosta,; J. Totz,; Z. H. Wang,; W. Shi, Real-time video super-resolution with spatio-temporal networks and motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4778-4787, 2017.
[8]
X. Tao,; H. Y. Gao,; R. J. Liao,; J. Wang,; J. Y. Jia, Detail-revealing deep video super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, 4472-4480, 2017.
[9]
M. S. M. Sajjadi,; R. Vemulapalli,; M. Brown, Frame-recurrent video super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6626-6634, 2018.
[10]
M. Haris,; G. Shakhnarovich,; N. Ukita, Recurrent back-projection network for video super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3897-3906, 2019.
[11]
Y. Jo,; S. W. Oh,; J. Kang,; S. J. Kim, Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3224-3232, 2018.
[12]
W. Z. Shi,; J. Caballero,; F. Huszar,; J. Totz,; A. P. Aitken,; R. Bishop,; D. Rueckert,; Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1874-1883, 2016.
[13]
Y. Huang,; W. Wang,; L. Wang, Bidirectional recurrent convolutional networks for multi-frame super-resolution. In: Proceedings of the Advances in Neural Information Processing Systems 28, 235-243, 2015.
[14]
D. Liu,; Z. W. Wang,; Y. C. Fan,; X. M. Liu,; Z. Y. Wang,; S. Y. Chang,; T. Huang, Robust video super-resolution with learned temporal dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, 2507-2515, 2017.
[15]
R. J. Liao,; X. Tao,; R. Y. Li,; Z. Y. Ma,; J. Y. Jia, Video super-resolution via deep draft-ensemble learning. In: Proceedings of the IEEE International Conference on Computer Vision, 531-539, 2015.
[16]
F. A. Gers,; J. Schmidhuber,; F. Cummins, Learning to forget: Continual prediction with LSTM. Neural Computation Vol. 12, No. 10, 2451-2471, 2000.
[17]
O. Makansi,; E. Ilg,; T. Brox, End-to-end learning of video super-resolution with motion compensation. In: Pattern Recognition. Lecture Notes in Computer Science, Vol. 10496. V. Roth,; T. Vetter, Eds. Springer Cham, 203-214, 2017.
[18]
M. Irani,; S. Peleg, Improving resolution by image registration. CVGIP: Graphical Models and Image Processing Vol. 53, No. 3, 231-239, 1991.
[19]
M. Irani,; S. Peleg, Motion analysis for image enhancement: Resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation Vol. 4, No. 4, 324-335, 1993.
[20]
C. Ledig,; L. Theis,; F. Huszar,; J. Caballero,; A. Cunningham,; A. Acosta,; A. Aitken,; A. Tejani,; J. Totz,; Z. et al. Wang, Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4681-4690, 2017.
[21]
Ren, H.; Fang, X. Recurrent back-projection network for video super-resolution. In: Final Project for MIT 6.819 Advances in Computer Vision, 1-6, 2018.
[22]
Z. H. Wang,; J. Chen,; S. C. H. Hoi, Deep learning for image super-resolution: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence DOI: 10.1109/TPAMI.2020.2982166, 2020.
[23]
M. Mathieu,; C. Couprie,; Y. LeCun, Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440, 2015.
[24]
J. Johnson,; A. Alahi,; F. F. Li, Perceptual losses for real-time style transfer and super-resolution. In: Computer Vision - ECCV 2016. Lecture Notes in Computer Science, Vol. 9906. B. Leibe,; J. Matas,; N. Sebe,; M. Welling, Eds. Springer Cham, 694-711, 2016.
[25]
A. Dosovitskiy,; T. Brox, Generating images with perceptual similarity metrics based on deep networks. In: Proceedings of the Advances in Neural Information Processing Systems 29, 658-666, 2016.
[26]
J. Bruna,; P. Sprechmann,; Y. LeCun, Super-resolution with deep convolutional sufficient statistics. In: Proceedings of the 4th International Conference on Learning Representations, 2016.
[27]
T. F. Xue,; B. A. Chen,; J. J. Wu,; D. L. Wei,; W. T. Freeman, Video enhancement with task-oriented flow. International Journal of Computer Vision Vol. 127, No. 8, 1106-1125, 2019.
[28]
C. Liu,; D. Q. Sun, A Bayesian approach to adaptive video super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 209-216, 2011.
[29]
R. Tsai, Multiframe image restoration and registration. Advance Computer Visual and Image Processing Vol. 1, 317-339, 1984.
[30]
J. C. Yang,; T. Huang, Image super-resolution: Historical overview and future challenges. In: Super-Resolution Imaging. P. Milanfar, Ed. CRC Press, 1-34, 2017.
[31]
Y. Tai,; J. Yang,; X. M. Liu, Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3147-3155, 2017.
[32]
J. Kim,; J. K. Lee,; K. M. Lee, Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1637-1645, 2016.
[33]
W. S. Lai,; J. B. Huang,; N. Ahuja,; M. H. Yang, Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 624-632, 2017.
[34]
A. Kappeler,; S. Yoo,; Q. Q. Dai,; A. K. Katsaggelos, Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging Vol. 2, No. 2, 109-122, 2016.
[35]
J. Johnson,; A. Karpathy,; F. F. Li, DenseCap: Fully convolutional localization networks for dense captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4565-4574, 2016.
[36]
J. Mao,; W. Xu,; Y. Yang,; J. Wang,; Z. Huang,; A. Yuille, Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint arXiv:1412.6632, 2014.
[37]
H. N. Yu,; J. Wang,; Z. H. Huang,; Y. Yang,; W. Xu, Video paragraph captioning using hierarchical recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4584-4593, 2016.
[38]
J. Donahue,; L. A. Hendricks,; S. Guadarrama,; M. Rohrbach,; S. Venugopalan,; T. Darrell,; K. Saenko, Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2625-2634, 2015.
[39]
S. Venugopalan,; H. Xu,; J. Donahue,; M. Rohrbach,; R. Mooney,; K. Saenko, Translating videos to natural language using deep recurrent neural networks In: Proceedings of the Annual Conference of the North American Chapter of the ACL, 1494-1504, 2015.
[40]
X. Shi,; Z. Chen,; H. Wang,; D. Yeung,; W. Wong,; W. Woo, Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Proceedings of the Advances in Neural Information Processing Systems 28, 1-9, 2015.
[41]
M. Drulea,; S. Nedevschi, Total variation regularization of local-global optical flow. In: Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, 318-323, 2011.
[42]
K. M. He,; X. Y. Zhang,; S. Q. Ren,; J. Sun, Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, 1026-1034, 2015.
[43]
A. Hore,; D. Ziou, Image quality metrics: PSNR vs. SSIM. In: Proceedings of the 20th International Conference on Pattern Recognition, 2366-2369, 2010.
[44]
M.-H. Cheng,; N.-W. Lin,; K.-S. Hwang,; J.-H. Jeng, Fast video super-resolution using artificial neural networks. In: Proceedings of the 8th International Symposium on Communication Systems, Networks & Digital Signal Processing, 1-4, 2012.
[45]
Z. Wang,; A. C. Bovik, A universal image quality index. IEEE Signal Processing Letters Vol. 9, No. 3, 81-84, 2002.
[46]
L. Gatys,; A. S. Ecker,; M. Bethge, Texture synthesis using convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems 28, 262-270, 2015.
[47]
K. Simonyan,; A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[48]
I. Goodfellow,; J. Pouget-Abadie,; M. Mirza,; B. Xu,; D. Warde-Farley,; S. Ozair,; A. Courville,; Y. Bengio, Generative adversarial nets. In: Proceedings of the Advances in Neural Information Processing Systems 27, 2672-2680, 2014.
[49]
H. A. Aly,; E. Dubois, Image up-sampling using total-variation regularization with a new observation model. IEEE Transactions on Image Processing Vol. 14, No. 10, 1647-1659, 2005.
[50]
J. Hany,; G. Walters, Hands-On Generative Adversarial Networks with PyTorch 1. x: Implement next-generation neural networks to build powerful GAN models using Python. Packt Publishing Ltd., 2019.