ML Anthology

Learning to Super Resolve Intensity Images from Events

S. I. Mohammad Mostafavi, Jonghyun Choi, Kuk-Jin Yoon
CVPR 2020
doi:10.1109/CVPR42600.2020.00284 /cvpr/2020/i2020cvpr-learning/

Abstract

An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images directly from the event stream. We evaluate our algorithm on both simulated and real-world sequences and verify that it captures fine details of a scene and outperforms the combination of the state-of-the-art event to image algorithms with the state-of-the-art super resolution schemes in many quantitative measures by large margins. We further extend our method by using the active sensor pixel (APS) frames or reconstructing images iteratively.

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Cite

Text

Mohammad Mostafavi et al. "Learning to Super Resolve Intensity Images from Events." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00284

Markdown

[Mohammad Mostafavi et al. "Learning to Super Resolve Intensity Images from Events." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/i2020cvpr-learning/) doi:10.1109/CVPR42600.2020.00284

BibTeX

@inproceedings{i2020cvpr-learning,
  title     = {{Learning to Super Resolve Intensity Images from Events}},
  author    = {Mohammad Mostafavi, S. I. and Choi, Jonghyun and Yoon, Kuk-Jin},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00284},
  url       = {https://mlanthology.org/cvpr/2020/i2020cvpr-learning/}
}