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Enhanced Adaptive Dense Connection Single Image Super-Resolution

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12537))

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Abstract

Increasing model size often results in improved performance on super-resolution reconstruction. However, at some point large model cannot SR huge images due to GPU/TPU memory limitations. In this paper, to address this problem, we present Block-Reconstruction(BR) strategy to improve the reconstruction quality of large images, which lower memory consumption. Meanwhile, we propose an enhanced adaptive dense connection super resolution reconstruction network(EDCSR) that has 89M parameters. In AIM2020 Real Image Super-Resolution Challenge, we won the second place in Track 1 and Track 2, and the third place in Track 3.

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References

  1. Anwar, S., Barnes, N.: Densely residual laplacian super-resolution. arXiv preprint arXiv:1906.12021 (2019)

  2. Chen, C., Xiong, Z., Tian, X., Zha, Z.J., Wu, F.: Camera lens super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1652–1660 (2019)

    Google Scholar 

  3. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  4. Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25

    Chapter  Google Scholar 

  5. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  6. Gao, X., Zhang, K., Tao, D., Li, X.: Image super-resolution with sparse neighbor embedding. IEEE Trans. Image Process. 21(7), 3194–3205 (2012)

    Article  MathSciNet  Google Scholar 

  7. Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1604–1613 (2019)

    Google Scholar 

  8. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  9. Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models Image Proc. 53(3), 231–239 (1991)

    Google Scholar 

  10. Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  11. Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016)

    Google Scholar 

  12. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  13. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)

    Google Scholar 

  14. Liu, J., Zhang, W., Tang, Y., Tang, J., Wu, G.: Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2359–2368 (2020)

    Google Scholar 

  15. Liu, X., Zhao, D., Xiong, R., Ma, S., Gao, W., Sun, H.: Image interpolation via regularized local linear regression. IEEE Trans. Image Process. 20(12), 3455–3469 (2011)

    Article  MathSciNet  Google Scholar 

  16. Patti, A.J., Sezan, M.I., Tekalp, A.M.: Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans. Image Process. 6(8), 1064–1076 (1997)

    Article  Google Scholar 

  17. Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996)

    Article  Google Scholar 

  18. Shi, W., et al.: 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, pp. 1874–1883 (2016)

    Google Scholar 

  19. Wei, P., Lu, H., Timofte, R., Lin, L., Zuo, W., et al.: AIM 2020 challenge on real image super-resolution. In: European Conference on Computer Vision Workshops (2020)

    Google Scholar 

  20. Wei, P., et al.: Component divide-and-conquer for real-world image super-resolution (2020)

    Google Scholar 

  21. Xie, T., Yang, X., Jia, Y., Zhu, C., Xiaochuan, L.: Adaptive densely connected single image super-resolution. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3432–3440. IEEE (2019)

    Google Scholar 

  22. Xu, X., Ma, Y., Sun, W.: Towards real scene super-resolution with raw images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1723–1731 (2019)

    Google Scholar 

  23. Yang, S., Wang, M., Chen, Y., Sun, Y.: Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans. Image Process. 21(9), 4016–4028 (2012)

    Article  MathSciNet  Google Scholar 

  24. Yoo, J., Ahn, N., Sohn, K.A.: Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8375–8384 (2020)

    Google Scholar 

  25. Yu, J., et al.: Wide activation for efficient and accurate image super-resolution. arXiv preprint arXiv:1808.08718 (2018)

  26. Zhang, K., Zuo, W., Zhang, L.: Deep plug-and-play super-resolution for arbitrary blur kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1671–1681 (2019)

    Google Scholar 

  27. Zhang, X., Wu, X.: Image interpolation by adaptive 2-d autoregressive modeling and soft-decision estimation. IEEE Trans. Image Process. 17(6), 887–896 (2008)

    Article  MathSciNet  Google Scholar 

  28. Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3762–3770 (2019)

    Google Scholar 

  29. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)

    Google Scholar 

  30. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472–2481 (2018)

    Google Scholar 

  31. Zhou, F., Yang, W., Liao, Q.: Interpolation-based image super-resolution using multisurface fitting. IEEE Trans. Image Process. 21(7), 3312–3318 (2012)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. China Electronic Technology Cyber Security Co., Ltd., Chengdu, China

    Tangxin Xie, Jing Li, Yi Shen, Yu Jia, Jialiang Zhang & Bing Zeng

Authors
  1. Tangxin Xie
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  2. Jing Li
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  3. Yi Shen
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  4. Yu Jia
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  5. Jialiang Zhang
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  6. Bing Zeng
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Corresponding author

Correspondence to Tangxin Xie .

Editor information

Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Xie, T., Li, J., Shen, Y., Jia, Y., Zhang, J., Zeng, B. (2020). Enhanced Adaptive Dense Connection Single Image Super-Resolution. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12537. Springer, Cham. https://doi.org/10.1007/978-3-030-67070-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-67070-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67069-6

  • Online ISBN: 978-3-030-67070-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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