Summary of E2hqv: High-quality Video Generation From Event Camera Via Theory-inspired Model-aided Deep Learning, by Qiang Qu et al.
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
by Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel events-to-video (E2V) paradigm called E2HQV is proposed to generate high-quality video frames from event-streams captured by bio-inspired event cameras or dynamic vision sensors. This approach leverages a model-aided deep learning framework, combining theory-inspired E2V modeling with fundamental imaging principles of event cameras. A temporal shift embedding module is designed to address state-reset issues in recurrent components. Comprehensive evaluations on real-world datasets demonstrate E2HQV’s superiority over state-of-the-art approaches, surpassing the second best by over 40% for some metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary E2HQV is a new way to turn event-streams into video frames that are easy to understand. Event cameras can capture very fast changes in brightness, but this makes it hard to create good videos from these events. Current solutions rely too much on machine learning and don’t use the underlying rules of how event cameras work. E2HQV uses both the rules and machine learning to create better videos. It also has a special module to help with state-reset issues in recurrent components. This approach is tested on real-world data and performs much better than previous methods. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Machine learning
