Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields. Event cameras are more sensitive to junction leakage current and photocurrent as they output differential signals, losing the smoothing function of the integral imaging process in the RGB camera. The logarithmic conversion further amplifies noise, especially in low-contrast conditions. Recently, researchers proposed a series of datasets and evaluation metrics but limitations remain: 1) the existing datasets are small in scale and insufficient in noise diversity, which cannot reflect the authentic working environments of event cameras; and 2) the existing denoising evaluation metrics are mostly referenced evaluation metrics, relying on APS information or manual annotation. To address the above issues, we construct a large-scale event denoising dataset (multilevel benchmark for event denoising, E-MLB) for the first time, which consists of 100 scenes, each with four noise levels, that is 12 times larger than the largest existing denoising dataset. We also propose the first nonreference event denoising metric, the event structural ratio (ESR), which measures the structural intensity of given events. ESR is inspired by the contrast metric, but is independent of the number of events and projection direction. Based on the proposed benchmark and ESR, we evaluate the most representative denoising algorithms, including classic and SOTA, and provide denoising baselines under various scenes and noise levels. The corresponding results and codes are available at https://github.com/KugaMaxx/cuke-emlb.

翻译：事件相机（如动态视觉传感器DVS）是一种受生物启发的视觉传感器，在高动态范围、低延迟和低功耗方面优于传统相机，在众多领域展现出巨大应用潜力。由于事件相机输出差分信号，失去了RGB相机积分成像过程的平滑功能，因此对结漏电流和光电流更为敏感。对数转换进一步放大了噪声，尤其在低对比度条件下。近期，研究者提出了一系列数据集和评估指标，但仍存在局限：1）现有数据集规模小、噪声多样性不足，无法反映事件相机的真实工作环境；2）现有去噪评估指标大多为有参考指标，依赖APS信息或人工标注。为解决上述问题，我们首次构建了大规模事件去噪数据集（事件去噪多层次基准，E-MLB），包含100个场景，每个场景具有四个噪声等级，规模比现有最大去噪数据集大12倍。我们还提出了首个无参考事件去噪指标——事件结构比（ESR），用于衡量给定事件的结构强度。ESR受对比度指标启发，但与事件数量和投影方向无关。基于所提出的基准和ESR，我们评估了包括经典方法和当前最优方法在内的最具代表性去噪算法，并提供了多种场景和噪声等级下的去噪基线。相应结果和代码可在https://github.com/KugaMaxx/cuke-emlb获取。