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Bringing Rolling Shutter Images Alive with Dual Reversed Distortion

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera. This means that the information of each instant GS frame is partially, yet sequentially, embedded into the row-dependent distortion. Inspired by this fact, we address the challenging task of reversing this process, i.e., extracting undistorted GS frames from images suffering from RS distortion. However, since RS distortion is coupled with other factors such as readout settings and the relative velocity of scene elements to the camera, models that only exploit the geometric correlation between temporally adjacent images suffer from poor generality in processing data with different readout settings and dynamic scenes with both camera motion and object motion. In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task. Grounded on the symmetric and complementary nature of dual reversed distortion, we develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time. Extensive experimental results demonstrate that IFED is superior to naive cascade schemes, as well as the state-of-the-art which utilizes adjacent RS images. Most importantly, although it is trained on a synthetic dataset, IFED is shown to be effective at retrieving GS frame sequences from real-world RS distorted images of dynamic scenes. Code is available at https://github.com/zzh-tech/Dual-Reversed-RS.

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References

  1. Albl, C., Kukelova, Z., Larsson, V., Polic, M., Pajdla, T., Schindler, K.: From two rolling shutters to one global shutter. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2505–2513 (2020)

    Google Scholar 

  2. Baker, S., Bennett, E., Kang, S.B., Szeliski, R.: Removing rolling shutter wobble. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2392–2399. IEEE (2010)

    Google Scholar 

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

    Google Scholar 

  4. Choi, M., Kim, H., Han, B., Xu, N., Lee, K.M.: Channel attention is all you need for video frame interpolation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10663–10671 (2020)

    Google Scholar 

  5. Dai, Y., Li, H., Kneip, L.: Rolling shutter camera relative pose: generalized epipolar geometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4132–4140 (2016)

    Google Scholar 

  6. Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758–2766 (2015)

    Google Scholar 

  7. Fan, B., Dai, Y.: Inverting a rolling shutter camera: bring rolling shutter images to high framerate global shutter video. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4228–4237 (2021)

    Google Scholar 

  8. Fan, B., Dai, Y., He, M.: Sunet: symmetric undistortion network for rolling shutter correction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4541–4550 (2021)

    Google Scholar 

  9. Forssén, P.E., Ringaby, E.: Rectifying rolling shutter video from hand-held devices. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 507–514. IEEE (2010)

    Google Scholar 

  10. Grundmann, M., Kwatra, V., Castro, D., Essa, I.: Calibration-free rolling shutter removal. In: 2012 IEEE International Conference on Computational Photography (ICCP), pp. 1–8. IEEE (2012)

    Google Scholar 

  11. Huang, Z., Zhang, T., Heng, W., Shi, B., Zhou, S.: Rife: real-time intermediate flow estimation for video frame interpolation. arXiv preprint arXiv:2011.06294 (2020)

  12. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462–2470 (2017)

    Google Scholar 

  13. 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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9000–9008 (2018)

    Google Scholar 

  14. Jin, M., Hu, Z., Favaro, P.: Learning to extract flawless slow motion from blurry videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8112–8121 (2019)

    Google Scholar 

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

    Google Scholar 

  16. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  17. Kalluri, T., Pathak, D., Chandraker, M., Tran, D.: Flavr: flow-agnostic video representations for fast frame interpolation. arXiv preprint arXiv:2012.08512 (2020)

  18. Lin, S., et al.: Learning event-driven video deblurring and interpolation. In: European Conference on Computer Vision, vol. 3 (2020)

    Google Scholar 

  19. Litwiller, D.: CCD vs. CMOS. Photonics Spectra 35(1), 154–158 (2001)

    Google Scholar 

  20. Liu, P., Cui, Z., Larsson, V., Pollefeys, M.: Deep shutter unrolling network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5941–5949 (2020)

    Google Scholar 

  21. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

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

    Google Scholar 

  23. Niklaus, S., Liu, F.: Softmax splatting for video frame interpolation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5437–5446 (2020)

    Google Scholar 

  24. Oth, L., Furgale, P., Kneip, L., Siegwart, R.: Rolling shutter camera calibration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1360–1367 (2013)

    Google Scholar 

  25. Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., Dai, Y.: Bringing a blurry frame alive at high frame-rate with an event camera. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6820–6829 (2019)

    Google Scholar 

  26. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)

  27. Purkait, P., Zach, C., Leonardis, A.: Rolling shutter correction in manhattan world. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 882–890 (2017)

    Google Scholar 

  28. Purohit, K., Shah, A., Rajagopalan, A.: Bringing alive blurred moments. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6830–6839 (2019)

    Google Scholar 

  29. Rengarajan, V., Balaji, Y., Rajagopalan, A.: Unrolling the shutter: CNN to correct motion distortions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2291–2299 (2017)

    Google Scholar 

  30. Rengarajan, V., Rajagopalan, A.N., Aravind, R.: From bows to arrows: rolling shutter rectification of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2773–2781 (2016)

    Google Scholar 

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

    Google Scholar 

  32. Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934–8943 (2018)

    Google Scholar 

  33. Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402–419. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_24

    Chapter  Google Scholar 

  34. Vasu, S., Rajagopalan, A., et al.: Occlusion-aware rolling shutter rectification of 3D scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 636–645 (2018)

    Google Scholar 

  35. Yang, X., Xiang, W., Zeng, H., Zhang, L.: Real-world video super-resolution: a benchmark dataset and a decomposition based learning scheme. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4781–4790 (2021)

    Google Scholar 

  36. Zhong, Z., Gao, Y., Zheng, Y., Zheng, B.: Efficient spatio-temporal recurrent neural network for video deblurring. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 191–207. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_12

    Chapter  Google Scholar 

  37. Zhuang, B., Cheong, L.F., Hee Lee, G.: Rolling-shutter-aware differential SFM and image rectification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 948–956 (2017)

    Google Scholar 

  38. Zhuang, B., Tran, Q.H., Ji, P., Cheong, L.F., Chandraker, M.: Learning structure-and-motion-aware rolling shutter correction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4551–4560 (2019)

    Google Scholar 

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Acknowledgement

This work was supported by D-CORE Grant from Microsoft Research Asia, JSPS KAKENHI Grant Numbers 22H00529, and 20H05951, and JST, the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2108.

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Correspondence to Yinqiang Zheng .

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Zhong, Z. et al. (2022). Bringing Rolling Shutter Images Alive with Dual Reversed Distortion. 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 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-20071-7_14

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