Accurate image super-resolution using very deep convolutional networks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 1646-1654

  Kim, Jiwon ^a   Lee, Jung Kwon ^a   Lee, Kyoung Mu ^a  

^a South Korea   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; FACE RECOGNITION; OPTICAL RESOLVING POWER;

CONTEXTUAL INFORMATION; CONVERGENCE SPEED; CONVOLUTIONAL NETWORKS; HIGHLY ACCURATE; IMAGE SUPER RESOLUTIONS; NETWORK STRUCTURES; TRAINING PROCEDURES; VISUAL IMPROVEMENTS;

PATTERN RECOGNITION;

EID: 84986325587     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.182     Document Type: Conference Paper

References (26)

1. Y. Bengio, I. J. Goodfellow, and A. Courville. Deep learning. Book in preparation for MIT Press, 2015.
2. Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on, 5 (2): 157-166, 1994.
3. M. Bevilacqua, A. Roumy, C. Guillemot, and M.-L. Morel. Super-resolution using neighbor embedding of backprojection residuals. In Digital Signal Processing (DSP), 2013 18th International Conference on, pages 1-8. IEEE, 2013.
4. H. Chang, D.-Y. Yeung, and Y. Xiong. Super-resolution through neighbor embedding. In CVPR, 2004.
5. C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV. 2014.
6. C. Dong, C. C. Loy, K. He, and X. Tang. Image superresolution using deep convolutional networks. TPAMI, 2015.
7. C. E. Duchon. Lanczos filtering in one and two dimensions. Journal of Applied Meteorology, 18 (8): 1016-1022, 1979.
8. W. T. Freeman, E. C. Pasztor, and O. T. Carmichael. Learning low-level vision. International journal of computer vision, 40 (1): 25-47, 2000.
9. D. Glasner, S. Bagon, and M. Irani. Super-resolution from a single image. In ICCV, 2009.
10. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR, abs/1502. 01852, 2015.
11. J.-B. Huang, A. Singh, and N. Ahuja. Single image superresolution using transformed self-exemplars. In CVPR, 2015.
12. M. Irani and S. Peleg. Improving resolution by image registration. CVGIP: Graphical models and image processing, 53 (3): 231-239, 1991.
13. K. I. Kim and Y. Kwon. Single-image super-resolution using sparse regression and natural image prior. TPAMI, 2010.
14. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86 (11): 2278-2324, 1998.
15. C. G. Marco Bevilacqua, Aline Roumy and M.-L. A. Morel. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In BMVC, 2012.
16. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.
17. R. Pascanu, T. Mikolov, and Y. Bengio. On the difficulty of training recurrent neural networks. In ICML, 2013.
18. S. Schulter, C. Leistner, and H. Bischof. Fast and accurate image upscaling with super-resolution forests. In CVPR, 2015.
19. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
20. J. Sun, Z. Xu, and H.-Y. Shum. Image super-resolution using gradient profile prior. In CVPR, 2008.
21. R. Timofte, V. De, and L. V. Gool. Anchored neighborhood regression for fast example-based super-resolution. In ICCV, 2013.
22. R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted anchored neighborhood regression for fast super-resolution. In ACCV, 2014.
23. A. Vedaldi and K. Lenc. Matconvnet-convolutional neural networks for matlab. CoRR, abs/1412. 4564, 2014.
24. C.-Y. Yang and M.-H. Yang. Fast direct super-resolution by simple functions. In ICCV, 2013.
25. J. Yang, J. Wright, T. S. Huang, and Y. Ma. Image superresolution via sparse representation. TIP, 2010.
26. R. Zeyde, M. Elad, and M. Protter. On single image scale-up using sparse-representations. In Curves and Surfaces, pages 711-730. Springer, 2012.