Abstract
Event cameras demonstrate unique characteristics such as high temporal resolution, low latency, and high dynamic range to improve performance for various image enhancement tasks. However, event streams cannot be applied to neural networks directly due to their sparse nature. To integrate events into traditional computer vision algorithms, an appropriate event representation is desirable, while existing voxel grid and event stack representations are less effective in encoding motion and temporal information. This paper presents a novel event representation named Neural Event STack (NEST), which satisfies physical constraints and encodes comprehensive motion and temporal information sufficient for image enhancement. We apply our representation on multiple tasks, which achieves superior performance on image deblurring and image super-resolution than state-of-the-art methods on both synthetic and real datasets. And we further demonstrate the possibility to generate high frame rate videos with our novel event representation.
Project page: https://github.com/ChipsAhoyM/NEST.
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Notes
- 1.
Detailed D-Net and S-Net configurations are in the supplementary material.
- 2.
\(*\) denotes retraining on our training dataset.
- 3.
Qualitative comparison between eSL-Net and NEST+eSL on deblurring and SR applications can be found in the supplementary material.
References
Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., Andreopoulos, Y.: Graph-based object classification for neuromorphic vision sensing. In: Proc. of International Conference on Computer Vision. pp. 491–501 (2019)
Brandli, C., Berner, R., Yang, M., Liu, S.C., Delbruck, T.: A 240 \(\times \) 180 130 dB 3 \(\mu \)s latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits 49(10), 2333–2341 (2014)
Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: A differentiable recurrent surface for asynchronous event-based data. In: Proc. of European Conference on Computer Vision. pp. 136–152 (2020)
Chen, H., Teng, M., Shi, B., Wang, Y., Huang, T.: Learning to deblur and generate high frame rate video with an event camera. arXiv preprint arXiv:2003.00847 (2020)
Duan, P., Wang, Z.W., Zhou, X., Ma, Y., Shi, B.: EventZoom: Learning to denoise and super resolve neuromorphic events. In: Proc. of Computer Vision and Pattern Recognition. pp. 12824–12833 (2021)
Gehrig, D., Loquercio, A., Derpanis, K.G., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: Proc. of International Conference on Computer Vision. pp. 5633–5643 (2019)
Han, J., Yang, Y., Zhou, C., Xu, C., Shi, B.: EvIntSR-Net: Event guided multiple latent frames reconstruction and super-resolution. In: Proc. of International Conference on Computer Vision. pp. 4882–4891 (2021)
Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: Proc. of Computer Vision and Pattern Recognition. pp. 3897–3906 (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proc. of Computer Vision and Pattern Recognition. pp. 4700–4708 (2017)
I., S.M.M., Choi, J., Yoon, K.: Learning to super resolve intensity images from events. In: Proc. of Computer Vision and Pattern Recognition. pp. 2765–2773 (2020)
Jiang, Z., Zhang, Y., Zou, D., Ren, J., Lv, J., Liu, Y.: Learning event-based motion deblurring. In: Proc. of Computer Vision and Pattern Recognition. pp. 3320–3329 (2020)
Jing, Y., Yang, Y., Wang, X., Song, M., Tao, D.: Turning frequency to resolution: Video super-resolution via event cameras. In: Proc. of Computer Vision and Pattern Recognition. pp. 7772–7781 (2021)
Kingma, D.P., Ba, J.: ADAM: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lagorce, X., Orchard, G., Galluppi, F., Shi, B.E., Benosman, R.B.: Hots: A hierarchy of event-based time-surfaces for pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(7), 1346–1359 (2016)
Li, Y., Zhou, H., Yang, B., Zhang, Y., Cui, Z., Bao, H., Zhang, G.: Graph-based asynchronous event processing for rapid object recognition. In: Proc. of International Conference on Computer Vision. pp. 934–943 (2021)
Lichtsteiner, P., Posch, C., Delbruck, T.: A 128 \(\times \) 128 120 dB 15 \(\mu \)s latency asynchronous temporal contrast vision sensor. IEEE Journal of Solid-State Circuits 43(2), 566–576 (2008)
Lin, S., Zhang, J., Pan, J., Jiang, Z., Zou, D., Wang, Y., Chen, J., Ren, J.: Learning event-driven video deblurring and interpolation. In: Proc. of European Conference on Computer Vision. pp. 695–710 (2020)
Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.: Structure-preserving super resolution with gradient guidance. In: Proc. of Computer Vision and Pattern Recognition. pp. 7766–7775 (2020)
Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., Mu Lee, K.: Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In: Proc. of Computer Vision and Pattern Recognition Workshops. pp. 1996–2005 (2019)
Neil, D., Pfeiffer, M., Liu, S.C.: Phased LSTM: Accelerating recurrent network training for long or event-based sequences. In: Proc. of Neural Information Processing Systems. pp. 3882–3890 (2016)
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: Proc. of Computer Vision and Pattern Recognition. pp. 6820–6829 (2019)
Rebecq, H., Gehrig, D., Scaramuzza, D.: ESIM: an open event camera simulator. In: Conference on Robot Learning. pp. 969–982 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Proc. of International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 234–241 (2015)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115(3), 211–252 (2015)
Sekikawa, Y., Hara, K., Saito, H.: Eventnet: Asynchronous recursive event processing. In: Proc. of Computer Vision and Pattern Recognition. pp. 3887–3896 (2019)
Shang, W., Ren, D., Zou, D., Ren, J.S., Luo, P., Zuo, W.: Bringing events into video deblurring with non-consecutively blurry frames. In: Proc. of International Conference on Computer Vision. pp. 4531–4540 (2021)
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proc. of Computer Vision and Pattern Recognition. pp. 1874–1883 (2016)
Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Proc. of Neural Information Processing Systems. pp. 802–810 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: HATS: Histograms of averaged time surfaces for robust event-based object classification. In: Proc. of Computer Vision and Pattern Recognition. pp. 1731–1740 (2018)
Wang, B., He, J., Yu, L., Xia, G.S., Yang, W.: Event enhanced high-quality image recovery. In: Proc. of European Conference on Computer Vision (2020)
Wang, L., I., S.M.M., Ho, Y., Yoon, K.: Event-based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks. In: Proc. of Computer Vision and Pattern Recognition. pp. 10081–10090 (2019)
Wang, L., Kim, T.K., Yoon, K.J.: EventSR: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In: Proc. of Computer Vision and Pattern Recognition. pp. 8312–8322 (2020)
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change Loy, C.: ESRGAN: Enhanced super-resolution generative adversarial networks. In: Proc. of European Conference on Computer Vision Workshops. pp. 63–79 (2018)
Yao, M., Gao, H., Zhao, G., Wang, D., Lin, Y., Yang, Z., Li, G.: Temporal-wise attention spiking neural networks for event streams classification. In: Proc. of International Conference on Computer Vision. pp. 10221–10230 (2021)
Zhong, Z., Gao, Y., Zheng, Y., Zheng, B.: Efficient spatio-temporal recurrent neural network for video deblurring. In: Proc. of European Conference on Computer Vision. pp. 191–207 (2020)
Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based learning of optical flow, depth, and egomotion. In: Proc. of Computer Vision and Pattern Recognition. pp. 989–997 (2019)
Acknowledgement
This work was supported by National Key R &D Program of China (2021ZD0109803) and National Natural Science Foundation of China under Grant No. 62136001, 62088102.
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Teng, M., Zhou, C., Lou, H., Shi, B. (2022). NEST: Neural Event Stack for Event-Based Image Enhancement. 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 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_38
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