Abstract
The asynchronous time-based neuromorphic image sensor ATIS is an array of autonomously operating pixels able to encode luminance information with an exceptionally high dynamic range (>143 dB). This paper introduces an event-based methodology to display data from this type of event-based imagers, taking into account the large dynamic range and high temporal accuracy that go beyond available mainstream display technologies. We introduce an event-based tone mapping methodology for asynchronously acquired time encoded gray-level data. A global and a local tone mapping operator are proposed. Both are designed to operate on a stream of incoming events rather than on time frame windows. Experimental results on real outdoor scenes are presented to evaluate the performance of the tone mapping operators in terms of quality, temporal stability, adaptation capability, and computational time.
Highlights
The human visual system can perceive a dynamic range of 160 dB overall (Hood, 1998), discerning details both in bright sunlight and in dim starlight
We address the case of gray-level tone mapping since this is the type of signal provided by the asynchronous time-based neuromorphic image sensor (ATIS), though the great majority of tone mapping operators are developed for color images
A tone mapping operator which uses different log-bases was defined by Drago et al (2003) for the tone mapping of images, though there is a parameter to adjust for optimal results
Summary
The human visual system can perceive a dynamic range of 160 dB overall (Hood, 1998), discerning details both in bright sunlight and in dim starlight. The development of high dynamic range (HDR) image and video acquisition systems is an active research topic. Such acquisition systems generally follow one of two paradigms: In one approach, the image sensors use non-linear compressive transfer characteristics, either physical logarithmic relations or piece-wise linear functions. Data from multiple linear captures are combined to obtain a single HDR frame (Yang et al, 1999) Both approaches generally lead to increased complexity in acquiring and processing the raw sensor data, limiting the achievable frame rate and the temporal resolution of the acquired image data
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