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Towards Interpretable Video Super-Resolution via Alternating Optimization

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.

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Notes

  1. 1.

    Please see the detailed proof in the supplementary materials.

References

  1. Afonso, M.V., Bioucas-Dias, J.M., Figueiredo, M.A.: Fast image recovery using variable splitting and constrained optimization. IEEE Trans. Image Process. 19(9), 2345–2356 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Argaw, D.M., Kim, J., Rameau, F., Kweon, I.S.: Motion-blurred video interpolation and extrapolation. In: AAAI Conference on Artificial Intelligence, pp. 901–910 (2021)

    Google Scholar 

  3. Bao, W., Lai, W.S., Ma, C., Zhang, X., Gao, Z., Yang, M.H.: Depth-aware video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3703–3712 (2019)

    Google Scholar 

  4. Bao, W., Lai, W.S., Zhang, X., Gao, Z., Yang, M.H.: MEMC-Net: motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 933–948 (2019)

    Article  Google Scholar 

  5. Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4778–4787 (2017)

    Google Scholar 

  6. Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: BasicVSR: the search for essential components in video super-resolution and beyond. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4947–4956 (2021)

    Google Scholar 

  7. Chan, K.C., Zhou, S., Xu, X., Loy, C.C.: BasicVSR++: improving video super-resolution with enhanced propagation and alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5972–5981 (2022)

    Google Scholar 

  8. Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: International Conference on Image Processing, vol. 2, pp. 168–172 (1994)

    Google Scholar 

  9. Chiche, B.N., Frontera-Pons, J., Woiselle, A., Starck, J.L.: Deep unrolled network for video super-resolution. In: International Conference on Image Processing Theory, Tools and Applications, pp. 1–6 (2020)

    Google Scholar 

  10. Gilavert, C., Moussaoui, S., Idier, J.: Efficient Gaussian sampling for solving large-scale inverse problems using MCMC. IEEE Trans. Sig. Process. 63(1), 70–80 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Gupta, A., Aich, A., Roy-Chowdhury, A.K.: ALANET: adaptive latent attention network forjoint video deblurring and interpolation. arXiv preprint arXiv:2009.01005 (2020)

  12. Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3897–3906 (2019)

    Google Scholar 

  13. Hyun Kim, T., Mu Lee, K.: Generalized video deblurring for dynamic scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5426–5434 (2015)

    Google Scholar 

  14. Hyun Kim, T., Mu Lee, K., Scholkopf, B., Hirsch, M.: Online video deblurring via dynamic temporal blending network. In: IEEE International Conference on Computer Vision, pp. 4038–4047 (2017)

    Google Scholar 

  15. Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J.: Super SloMo: high quality estimation of multiple intermediate frames for video interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9000–9008 (2018)

    Google Scholar 

  16. Jin, M., Meishvili, G., Favaro, P.: Learning to extract a video sequence from a single motion-blurred image. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6334–6342 (2018)

    Google Scholar 

  17. Jo, Y., Oh, S.W., Kang, J., Kim, S.J.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3224–3232 (2018)

    Google Scholar 

  18. Kim, T.H., Nah, S., Lee, K.M.: Dynamic video deblurring using a locally adaptive blur model. IEEE Trans. Pattern Anal. Mach. Intell. 40(10), 2374–2387 (2017)

    Article  Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)

    Google Scholar 

  20. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN: blind motion deblurring using conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8183–8192 (2018)

    Google Scholar 

  21. Liang, J., et al.: VRT: a video restoration transformer. arXiv preprint arXiv:2201.12288 (2022)

  22. Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: image restoration using swin transformer. In: IEEE International Conference on Computer Vision Workshops, pp. 1833–1844 (2021)

    Google Scholar 

  23. Liang, J., et al.: Recurrent video restoration transformer with guided deformable attention. arXiv preprint arXiv:2206.02146 (2022)

  24. Liang, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Mutual affine network for spatially variant kernel estimation in blind image super-resolution. In: IEEE Conference on International Conference on Computer Vision, pp. 4096–4105 (2021)

    Google Scholar 

  25. Liu, C., Sun, D.: On Bayesian adaptive video super resolution. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 346–360 (2013)

    Article  Google Scholar 

  26. Liu, C., Sun, D.: On Bayesian adaptive video super resolution. IEEE Transactions on Pattern Anal. Mach. Intell. 36(2), 346–360 (2013)

    Article  Google Scholar 

  27. Liu, Z., Yeh, R.A., Tang, X., Liu, Y., Agarwala, A.: Video frame synthesis using deep voxel flow. In: IEEE International Conference on Computer Vision, pp. 4463–4471 (2017)

    Google Scholar 

  28. Long, G., Kneip, L., Alvarez, J.M., Li, H., Zhang, X., Yu, Q.: Learning image matching by simply watching video. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 434–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_26

    Chapter  Google Scholar 

  29. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

  30. Meyer, S., Wang, O., Zimmer, H., Grosse, M., Sorkine-Hornung, A.: Phase-based frame interpolation for video. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1418 (2015)

    Google Scholar 

  31. Mudenagudi, U., Banerjee, S., Kalra, P.K.: Space-time super-resolution using graph-cut optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 995–1008 (2010)

    Article  Google Scholar 

  32. Nah, S., et al.: NTIRE 2019 challenge on video deblurring and super-resolution: dataset and study. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  33. Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3883–3891 (2017)

    Google Scholar 

  34. Nah, S., Son, S., Lee, K.M.: Recurrent neural networks with intra-frame iterations for video deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8102–8111 (2019)

    Google Scholar 

  35. Niklaus, S., Liu, F.: Context-aware synthesis for video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1710 (2018)

    Google Scholar 

  36. Oh, J., Kim, M.: DeMFI: deep joint deblurring and multi-frame interpolation with flow-guided attentive correlation and recursive boosting. arXiv preprint arXiv:2111.09985 (2021)

  37. Pan, J., Bai, H., Dong, J., Zhang, J., Tang, J.: Deep blind video super-resolution. In: IEEE International Conference on Computer Vision, pp. 4811–4820 (2021)

    Google Scholar 

  38. Pan, J., Bai, H., Tang, J.: Cascaded deep video deblurring using temporal sharpness prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3043–3051 (2020)

    Google Scholar 

  39. Pollak Zuckerman, L., Naor, E., Pisha, G., Bagon, S., Irani, M.: Across scales & across dimensions: temporal super-resolution using deep internal learning. arXiv e-prints pp. arXiv-2003 (2020)

    Google Scholar 

  40. Robinson, M.D., Toth, C.A., Lo, J.Y., Farsiu, S.: Efficient Fourier-wavelet super-resolution. IEEE Trans. Image Process. 19(10), 2669–2681 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  41. Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6626–6634 (2018)

    Google Scholar 

  42. Shahar, O., Faktor, A., Irani, M.: Space-time super-resolution from a single video. IEEE (2011)

    Google Scholar 

  43. Shechtman, E., Caspi, Y., Irani, M.: Increasing space-time resolution in video. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 753–768. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47969-4_50

    Chapter  Google Scholar 

  44. Shen, W., Bao, W., Zhai, G., Chen, L., Min, X., Gao, Z.: Blurry video frame interpolation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5114–5123 (2020)

    Google Scholar 

  45. Šroubek, F., Kamenickỳ, J., Milanfar, P.: Superfast superresolution. In: IEEE International Conference on Image Processing, pp. 1153–1156 (2011)

    Google Scholar 

  46. Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1279–1288 (2017)

    Google Scholar 

  47. Takeda, H., Beek, P.v., Milanfar, P.: Spatiotemporal video upscaling using motion-assisted steering kernel (mask) regression. In: Mrak, M., Grgic, M., Kunt, M. (eds.) High-Quality Visual Experience. Signals and Communication Technology, pp. 245–274. Springer (2010). https://doi.org/10.1007/978-3-642-12802-8_10

  48. Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: IEEE International Conference on Computer Vision, pp. 4472–4480 (2017)

    Google Scholar 

  49. Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8174–8182 (2018)

    Google Scholar 

  50. Telleen, J., et al.: Synthetic shutter speed imaging. In: Computer Graphics Forum, vol. 26, pp. 591–598 (2007)

    Google Scholar 

  51. Tian, Y., Zhang, Y., Fu, Y., Xu, C.: TDAN: temporally-deformable alignment network for video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3360–3369 (2020)

    Google Scholar 

  52. Wang, L., Guo, Y., Lin, Z., Deng, X., An, W.: Learning for video super-resolution through HR optical flow estimation. In: Asian Conference on Computer Vision, pp. 514–529 (2018)

    Google Scholar 

  53. Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  54. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  55. Xiang, X., Tian, Y., Zhang, Y., Fu, Y., Allebach, J.P., Xu, C.: Zooming slow-mo: fast and accurate one-stage space-time video super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3370–3379 (2020)

    Google Scholar 

  56. Xiao, Z., Xiong, Z., Fu, X., Liu, D., Zha, Z.J.: Space-time video super-resolution using temporal profiles. In: ACM International Conference on Multimedia, pp. 664–672 (2020)

    Google Scholar 

  57. 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)

    Article  Google Scholar 

  58. Zamir, A.R., et al.: Feedback networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1308–1317 (2017)

    Google Scholar 

  59. Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3217–3226 (2020)

    Google Scholar 

  60. Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., Li, H.: Adversarial spatio-temporal learning for video deblurring. IEEE Trans. Image Process. 28(1), 291–301 (2018)

    Article  MathSciNet  Google Scholar 

  61. Zhao, N., Wei, Q., Basarab, A., Dobigeon, N., Kouamé, D., Tourneret, J.Y.: Fast single image super-resolution using a new analytical solution for l2–l2 problems. IEEE Trans. Image Process. 25(8), 3683–3697 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhou, S., Zhang, J., Pan, J., Xie, H., Zuo, W., Ren, J.: Spatio-temporal filter adaptive network for video deblurring. In: IEEE International Conference on Computer Vision, pp. 2482–2491 (2019)

    Google Scholar 

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Acknowledgements

This work was partly supported by Huawei Fund and the ETH Zürich Fund (OK).

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Authors and Affiliations

  1. Computer Vision Lab, ETH Zürich, Zürich, Switzerland

    Jiezhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang, Qin Wang, Yulun Zhang, Hao Tang & Luc Van Gool

  2. KU Leuven, Leuven, Belgium

    Luc Van Gool

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  1. Jiezhang Cao
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  2. Jingyun Liang
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  3. Kai Zhang
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  4. Wenguan Wang
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  5. Qin Wang
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  7. Hao Tang
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  8. Luc Van Gool
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Correspondence to Kai Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Cao, J. et al. (2022). Towards Interpretable Video Super-Resolution via Alternating Optimization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13678. Springer, Cham. https://doi.org/10.1007/978-3-031-19797-0_23

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