This paper introduces a new methodology to process asynchronously sampled image data captured by a new generation of biomimetic vision sensors. Unlike conventional cameras, these neuromorphic sensors acquire data not at fixed points in time for the entire array (frame-based) but sparse in space and time, i.e., pixel-individually and precisely timed only if new information is available (event-based). In this paper, we introduce a filtering methodology for asynchronously acquired gray-level data from an event-driven time-encoding imager. The paper first studies the properties of level-crossing sampling parameters in order to define threshold level properties and associated bandwidth needs. In a second stage, we introduce asynchronous linear and nonlinear filtering techniques. Examples are shown and examined on real data. Finally, the paper introduces a methodology to compare frame-based versus event-based computational costs. Implementations and experiments show that event-based gray-level filtering produces equivalent filtering accuracy as compared to frame-based ones. The main result of this work shows that, based on the number of operations to be carried out, beyond 3 frames per second (fps), event-based processing outperforms frame-based processing in terms of computational cost.