Login | Register

Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network

Title:

Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network

Esmaeilzehi, Alireza, Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 and Swamy, M.N.S. (2018) Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network. IEEE Access, 6 . pp. 59963-59974. ISSN 2169-3536

[img]
Preview
Text (application/pdf)
Ahmad-IEEE Access-2018.pdf - Published Version
Available under License Spectrum Terms of Access.
1MB

Official URL: http://dx.doi.org/10.1109/ACCESS.2018.2874442

Abstract

The features produced by the layers of a neural network become increasingly more sparse as the network gets deeper and consequently, the learning capability of the network is not further enhanced as the number of layers is increased. In this paper, a novel residual deep network, called CompNet, is proposed for the single image super resolution problem without an excessive increase in the network complexity. The idea behind the proposed network is to compose the residual signal that is more representative of the features produced by the different layers of the network and it is not as sparse. The proposed network is experimented on different benchmark datasets and is shown to outperform the state-of-the-art schemes designed to solve the super resolution problem.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Esmaeilzehi, Alireza and Ahmad, M. Omair and Swamy, M.N.S.
Journal or Publication:IEEE Access
Date:2018
Funders:
  • Concordia Open Access Author Fund
  • Natural Sciences and Engineering Research Council of Canada
  • Regroupment Strategique en Microelectronique du Quebec
Digital Object Identifier (DOI):10.1109/ACCESS.2018.2874442
Keywords:Image super resolution, residual learning, deep learning
ID Code:984719
Deposited By: KRISTA ALEXANDER
Deposited On:30 Nov 2018 20:17
Last Modified:30 Nov 2018 20:17

References:

1. S. C. Park, M. K. Park, M. G. Kang, "Super-resolution image reconstruction: A technical overview", IEEE Signal Process. Mag., vol. 20, pp. 21-36, May 2003.

2. J. Yang, J. Wright, T. S. Huang, Y. Ma, "Image super-resolution via sparse representation", IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010.

3. K.-W. Hung, W.-C. Siu, "Single-image super-resolution using iterative Wiener filter based on nonlocal means", Signal Process. Image Commun., vol. 39, pp. 26-45, Nov. 2015.

4. Y. LeCun, Y. Bengio, G. Hinton, "Deep learning", Nature, vol. 521, no. 7553, pp. 436-444, 2015.

5. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, Cambridge, MA, USA:MIT Press, 2016.

6. K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition", Proc. CVPR, pp. 770-778, 2016.

7. B. Li, Y. He, "An improved ResNet based on the adjustable shortcut connections", IEEE Access, vol. 6, pp. 18967-18974, 2018.

8. O. Russakovsky et al., "ImageNet large scale visual recognition challenge", Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015.

9. J. Long, E. Shelhamer, T. Darrell, "Fully convolutional networks for semantic segmentation", Proc. CVPR, pp. 3431-3440, 2015.

10. S.-J. Lee, T. Chen, L. Yu, C.-H. Lai, "Image classification based on the boost convolutional neural network", IEEE Access, vol. 6, pp. 12755-12768, 2018.

11. L. Zhang et al., "Improving semantic image segmentation with a probabilistic superpixel-based dense conditional random field", IEEE Access, vol. 6, pp. 15297-15310, 2018.

12. A. Krizhevsky, I. Sutskever, G. E. Hinton, "ImageNet classification with deep convolutional neural networks", Proc. NIPS, pp. 1097-1105, 2012.

13. M. D. Zeiler et al., "On rectified linear units for speech processing", Proc. ICASSP, pp. 3517-3521, 2013.

14. C. Dong, C. C. Loy, K. He, X. Tang, "Image super-resolution using deep convolutional networks", IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 295-307, Feb. 2015.

15. C. Dong, C. C. Loy, X. Tang, "Accelerating the super-resolution convolutional neural network", Proc. ECCV, pp. 391-407, 2016.

16. D. Liu, Z. Wang, B. Wen, J. Yang, W. Han, T. S. Huang, "Robust single image super-resolution via deep networks with sparse prior", IEEE Trans. Image Process., vol. 25, no. 7, pp. 3194-3207, Jul. 2016.

17. K. Gregor, Y. LeCun, "Learning fast approximations of sparse coding", Proc. ICML, pp. 399-406, 2010.

18. D. Liu, Z. Wang, N. Nasrabadi, T. Huang, "Learning a mixture of deep networks for single image super-resolution", Proc. ACCV, pp. 145-156, 2016.

19. J. Kim, J. K. Lee, K. M. Lee, "Accurate image super-resolution using very deep convolutional networks", Proc. CVPR, pp. 1646-1654, 2016.

20. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014, [online] Available: https://arxiv.org/abs/1409.1556.

21. Y. Liang, Z. Yang, K. Zhang, Y. He, J. Wang, N. Zheng, Single image super-resolution via a lightweight residual convolutional neural network, 2017, [online] Available: https://arxiv.org/abs/1703.08173.

22. E. J. Candès, M. B. Wakin, "An introduction to compressive sampling", IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.

23. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma, "Robust face recognition via sparse representation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.

24. A. Esmaeilzehi, H. A. Moghaddam, "Nonparametric kernel sparse representation-based classifier", Pattern Recognit. Lett., vol. 89, no. 4, pp. 46-52, 2017.

25. T. Peleg, M. Elad, "A statistical prediction model based on sparse representations for single image super-resolution", IEEE Trans. Image Process., vol. 23, no. 6, pp. 2569-2582, Jun. 2014.

26. W. Yang et al., "Deep edge guided recurrent residual learning for image super-resolution", IEEE Trans. Image Process., vol. 26, no. 12, pp. 5895-5907, Dec. 2017.

27. D. Martin, C. Fowlkes, D. Tal, J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", Proc. ICCV, pp. 416-423, 2001.

28. Y. Wang, L. Wang, H. Wang, P. Li, End-to-end image super-resolution via deep and shallow convolutional networks, 2016, [online] Available: https://arxiv.org/abs/1607.07680.

29. J. W. Woods, Multidimensional Signal Image and Video Processing and Coding, New York, NY, USA:Academic, 2001.

30. G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, Densely connected convolutional networks, 2018, [online] Available: https://arxiv.org/abs/1608.06993.

31. S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015, [online] Available: https://arxiv.org/abs/1502.03167.

32. R. Pascanu, T. Mikolov, Y. Bengio, On the difficulty of training recurrent neural networks, 2013, [online] Available: https://arxiv.org/abs/1211.5063.

33. C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, Understanding deep learning requires rethinking generalization, 2017, [online] Available: https://arxiv.org/abs/1611.03530.

34. K. He, X. Zhang, S. Ren, J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification", Proc. ICCV, pp. 1026-1034, 2015.

35. T. Doza, Incorporating Nesterov Momentum Into Adam, Oct. 2018, [online] Available: https://web.stanford.edu/~tdozat/files/TDozat-CS229-Paper.pdf.

36. Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity", IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.

37. F. Yu, V. Koltun, Multi-scale context aggregation by dilated convolutions, 2016, [online] Available: https://arxiv.org/abs/1511.07122.

38. D.-A. Clevert, T. Unterthiner, S. Hochreiter, Fast and accurate deep network learning by exponential linear units (ELUs), 2016, [online] Available: https://arxiv.org/abs/1511.07289.

39. M. Bevilacqua, A. Roumy, C. Guillemot, M.-L. Alberi-Morel, "Low-complexity single-image super-resolution based on nonnegative neighbor embedding", Proc. BMVC, pp. 135.1-135.10, 2012.

40. R. Zeyde, M. Elad, M. Protter, "On single image scale-up using sparse-representations" in Curves and Surfaces, Heidelberg, Germany:Springer, 2012.

41. F. Chollet, Keras, Oct. 2015, [online] Available: https://github.com/keras-team/keras.

42. M. Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Oct. 2015, [online] Available: https://www.tensorflow.org.

43. R. Timofte, V. De Smet, L. Van Gool, "A+: Adjusted anchored neighborhood regression for fast super-resolution", Proc. ACCV, pp. 111-126, 2014.

44. S. Schulter, C. Leistner, H. Bischof, "Fast and accurate image upscaling with super-resolution forests", Proc. CVPR, pp. 3791-3799, 2015.

45. B. Lim, S. Son, H. Kim, S. Nah, K. M. Lee, "Enhanced deep residual networks for single image super-resolution", Proc. CVPR, pp. 1132-1140, 2017.

46. C. Ledig et al., "Photo-realistic single image super-resolution using a generative adversarial network", Proc. CVPR, pp. 4681-4690, 2017.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top