  Event cameras are renowned for their high efficiency due to outputting a
sparse, asynchronous stream of events. However, they are plagued by noisy
events, especially in low light conditions. Denoising is an essential task for
event cameras, but evaluating denoising performance is challenging.
Label-dependent denoising metrics involve artificially adding noise to clean
sequences, complicating evaluations. Moreover, the majority of these metrics
are monotonic, which can inflate scores by removing substantial noise and valid
events. To overcome these limitations, we propose the first label-free and
non-monotonic evaluation metric, the area of the continuous contrast curve
(AOCC), which utilizes the area enclosed by event frame contrast curves across
different time intervals. This metric is inspired by how events capture the
edge contours of scenes or objects with high temporal resolution. An effective
denoising method removes noise without eliminating these edge-contour events,
thus preserving the contrast of event frames. Consequently, contrast across
various time ranges serves as a metric to assess denoising effectiveness. As
the time interval lengthens, the curve will initially rise and then fall. The
proposed metric is validated through both theoretical and experimental
evidence.
