EventAid：采用真实采集混合数据集的事件辅助图像/视频增强算法基准测试
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset
AI总结：
该研究聚焦5类事件辅助图像/视频增强任务，构建了真实采集的EventAid基准数据集，对前沿方法与主流事件模拟器开展统一评测，分析性能极限并提出未来研究方向。
摘要：
事件相机是一种新兴成像技术，在动态范围和传感速度上优于传统基于帧的成像传感器。事件相机与基于帧的相机组成的混合相机系统可补充传统图像帧丰富的纹理与色彩感知，实现高性能成像。借助事件相机，高质量图像/视频增强方法有望突破传统基于帧相机的限制，尤其是曝光时间、分辨率、动态范围和帧率限制。本文聚焦5项事件辅助图像与视频增强任务（即基于事件的视频重建、事件辅助高帧率视频重建、图像去模糊、图像超分辨率和高动态范围图像重建），分析了不同事件属性的影响，提供了一个带真实采集与真值标注的基准数据集、对现有最优方法的统一基准测试，以及对两种主流事件模拟器的评估。具体而言，本文利用“Event-RGB”多相机混合系统，兼顾场景多样性与时空同步性，采集了面向5项事件辅助图像/视频增强任务的真实采集评估数据集EventAid。我们进一步对现有最优算法进行了定量与视觉对比，开展了对照实验以分析事件辅助图像去模糊方法的性能极限，并讨论了开放性问题以启发未来研究。
英文摘要：
Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. Complementing the rich texture and color perception of traditional image frames, the hybrid camera system of event and frame-based cameras enables high-performance imaging. With the assistance of event cameras, high-quality image/video enhancement methods make it possible to break the limits of traditional frame-based cameras, especially exposure time, resolution, dynamic range, and frame rate limits. This paper focuses on five event-aided image and video enhancement tasks (i.e., event-based video reconstruction, event-aided high frame rate video reconstruction, image deblurring, image super-resolution, and high dynamic range image reconstruction), provides an analysis of the effects of different event properties, a real-captured and ground truth labeled benchmark dataset, a unified benchmarking of state-of-the-art methods, and an evaluation for two mainstream event simulators. In detail, this paper collects a real-captured evaluation dataset EventAid for five event-aided image/video enhancement tasks, by using "Event-RGB" multi-camera hybrid system, taking into account scene diversity and spatiotemporal synchronization. We further perform quantitative and visual comparisons for state-of-the-art algorithms, provide a controlled experiment to analyze the performance limit of event-aided image deblurring methods, and discuss open problems to inspire future research.
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