Advertisement

Video Super-Resolution with Recurrent Structure-Detail Network

Conference paper
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12357)

Abstract

Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the recurrent-based methods. In this work, we propose a novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame. It divides the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks. In addition, a hidden state adaptation module that allows the current frame to selectively use information from hidden state is introduced to enhance its robustness to appearance change and error accumulation. Extensive ablation study validate the effectiveness of the proposed modules. Experiments on several benchmark datasets demonstrate superior performance of the proposed method compared to state-of-the-art methods on video super-resolution. Code is available at https://github.com/junpan19/RSDN.

Keywords

Video super-resolution Recurrent neural network Two-stream block 

References

  1. 1.
    Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: CVPR (2017)Google Scholar
  2. 2.
    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_13CrossRefGoogle Scholar
  3. 3.
    Du, W., Wang, Y., Qiao, Y.: RPAN: an end-to-end recurrent pose-attention network for action recognition in videos. In: CVPR (2017)Google Scholar
  4. 4.
    Fuoli, D., Gu, S., Timofte, R.: Efficient video super-resolution through recurrent latent space propagation. CoRR abs/1909.08080 (2019)Google Scholar
  5. 5.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: AISTATS (2011)Google Scholar
  6. 6.
    Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: CVPR (2018)Google Scholar
  7. 7.
    Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: CVPR (2019)Google Scholar
  8. 8.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)Google Scholar
  9. 9.
    Huang, Y., Wang, W., Wang, L.: Bidirectional recurrent convolutional networks for multi-frame super-resolution. In: NeurIPS (2015)Google Scholar
  10. 10.
    Isobe, T., et al.: Video super-resolution with temporal group attention. In: CVPR (2020)Google Scholar
  11. 11.
    Jia, X., De Brabandere, B., Tuytelaars, T., Gool, L.V.: Dynamic filter networks. In: NeurIPS (2016)Google Scholar
  12. 12.
    Jo, Y., Wug Oh, S., Kang, J., Joo Kim, S.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: CVPR (2018)Google Scholar
  13. 13.
    Kappeler, A., Yoo, S., Dai, Q., Katsaggelos, A.K.: Video super-resolution with convolutional neural networks. IEEE Trans. Comput. Imaging 2(2), 109–122 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)Google Scholar
  15. 15.
    Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: CVPR (2016)Google Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
  17. 17.
    Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2599–2613 (2018)CrossRefGoogle Scholar
  18. 18.
    Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)Google Scholar
  19. 19.
    Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: CVPR Workshops (2017)Google Scholar
  20. 20.
    Liu, C., Sun, D.: On bayesian adaptive video super resolution. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 346–360 (2013)CrossRefGoogle Scholar
  21. 21.
    Liu, D., et al.: Robust video super-resolution with learned temporal dynamics. In: ICCV (2017)Google Scholar
  22. 22.
    Pan, J., et al.: Learning dual convolutional neural networks for low-level vision. In: CVPR (2018)Google Scholar
  23. 23.
    Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: CVPR (2018)Google Scholar
  24. 24.
    Singh, B., Marks, T.K., Jones, M., Tuzel, O., Shao, M.: A multi-stream bi-directional recurrent neural network for fine-grained action detection. In: CVPR (2016)Google Scholar
  25. 25.
    Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: ICCV (2017)Google Scholar
  26. 26.
    Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: Video restoration with enhanced deformable convolutional networks. In: CVPR Workshops (2019)Google Scholar
  27. 27.
    Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vis. 127(8), 1106–1125 (2019)CrossRefGoogle Scholar
  28. 28.
    Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q.: Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimed. 21(12), 3106–3121 (2019)CrossRefGoogle Scholar
  29. 29.
    Yi, P., Wang, Z., Jiang, K., Jiang, J., Ma, J.: Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In: ICCV (2019)Google Scholar
  30. 30.
    Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01234-2_18CrossRefGoogle Scholar
  31. 31.
    Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: CVPR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Noah’s Ark LabHuawei TechnologiesShenzhenChina
  3. 3.School of EieThe University of SydneySydneyAustralia

Personalised recommendations