Dynamic Reconstruction from Neuromorphic Data

TL;DR

This paper introduces an optimization-based method for reconstructing images and dynamics solely from neuromorphic event data, without relying on traditional images, enabling efficient processing of asynchronous sensor information.

Contribution

The work presents the first approach to reconstruct images directly from neuromorphic data without auxiliary image information, modeling each pixel temporally for improved accuracy.

Findings

Effective reconstruction demonstrated on real neuromorphic data

Outperforms previous methods combining images and events

Enables real-time processing of neuromorphic sensor data

Abstract

Unlike traditional cameras which synchronously register pixel intensity, neuromorphic sensors only register `changes' at pixels where a change is occurring asynchronously. This enables neuromorphic sensors to sample at a micro-second level and efficiently capture the dynamics. Since, only sequences of asynchronous event changes are recorded rather than brightness intensities over time, many traditional image processing techniques cannot be directly applied. Furthermore, existing approaches, including the ones recently introduced by the authors, use traditional images combined with neuromorphic event data to carry out reconstructions. The aim of this work is introduce an optimization based approach to reconstruct images and dynamics only from the neuromoprhic event data without any additional knowledge of the events. Each pixel is modeled temporally. The experimental results on real data highlight the efficacy of the presented approach, paving the way for efficient and accurate processing of neuromorphic sensor data in real-world applications.