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Towards Safer Transportation: a self-supervised learning approach for traffic video deraining

arXiv 2110.07379 ↗First posted 11 Oct 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement. However, traffic monitoring during inclement weather such as rain is a challenging task because video quality is corrupted by streaks of falling rain on the video image, and this hinders reliable characterization not only of the road environment but also of road-user behavior during such adverse weather events. This study proposes a two-stage self-supervised learning method to remove rain streaks in traffic videos. The first and second stages address intra- and inter-frame noise, respectively. The results indicated that the model exhibits satisfactory performance in terms of the image visual quality and the Peak Signal-Noise Ratio value.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 11 Oct 2021.

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15 of 46 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
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  10. 2016Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensationcited by 156
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  12. 2016Clearing the Skies: A deep network architecture for single-image rain removalcited by 136
3 more
  1. 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
  2. 2016Generalized Deep Image to Image Regressioncited by 14
  3. 2020First image then video: A two-stage network for spatiotemporal video denoisingcited by 3

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