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Event-Based Tone Mapping for Asynchronous Time-Based Image Sensor.

Camille Simon Chane¹, Sio-Hoi Ieng², Christoph Posch²

¹Pixium Vision Paris, France.

Frontiers in Neuroscience

|September 20, 2016

Summary

This summary is machine-generated.

This study presents a new method for displaying images from asynchronous time-based neuromorphic image sensors (ATIS). The event-based tone mapping technique effectively handles high dynamic range and temporal accuracy for better visual data representation.

Keywords:

AER HDR imaging neuromorphic vision silicon retina tone mapping

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Area of Science:

Neuromorphic Engineering
Computer Vision
Image Processing

Background:

Asynchronous time-based neuromorphic image sensors (ATIS) offer high dynamic range (>143 dB) and temporal accuracy.
Existing display technologies struggle to represent the full data range and speed of event-based imagers.
A need exists for specialized methods to visualize data from advanced neuromorphic sensors.

Purpose of the Study:

To introduce an event-based methodology for displaying data from ATIS.
To develop tone mapping operators that accommodate high dynamic range and temporal resolution.
To evaluate the performance of these operators for real-world outdoor scenes.

Main Methods:

An event-based tone mapping methodology is proposed for asynchronously acquired, time-encoded gray-level data.
Both global and local tone mapping operators are designed to process event streams directly.
The methodology avoids traditional time-frame windowing for continuous data processing.

Main Results:

The proposed tone mapping operators demonstrate effective handling of high dynamic range and temporal data.
Experimental results on outdoor scenes show good performance in terms of visual quality and temporal stability.
The operators exhibit adaptive capabilities and reasonable computational time.

Conclusions:

The developed event-based tone mapping methodology is suitable for displaying data from ATIS.
This approach overcomes limitations of mainstream display technologies for neuromorphic imaging.
The proposed operators provide a viable solution for visualizing high-performance event-based sensor data.