E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

• URL: http://arxiv.org/abs/2401.08117v1
• Date: Tue, 16 Jan 2024 05:10:50 GMT
• Title: E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
• Authors: Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, and Tongliang Liu
• Abstract summary: Bio-inspired event cameras or dynamic vision sensors are capable of capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. We propose textbfE2HQV, a novel E2V paradigm designed to produce high-quality video frames from events.
• Score: 53.63364311738552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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