E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Qu, Qiang Shen, Yiran Chen, Xiaoming Chung, Yuk Ying Liu, Tongliang |
| author_facet | Qu, Qiang Shen, Yiran Chen, Xiaoming Chung, Yuk Ying Liu, Tongliang |
| contents | The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose \textbf{E2HQV}, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40\% for some evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08117 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning Qu, Qiang Shen, Yiran Chen, Xiaoming Chung, Yuk Ying Liu, Tongliang Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Neural and Evolutionary Computing The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose \textbf{E2HQV}, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40\% for some evaluation metrics. |
| title | E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.08117 |