Dynamic reconstruction from neuromorphic data

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 microsecond 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 to introduce an optimization-based approach to reconstruct images and dynamics only from the neuromorphic event data without any additional knowledge of the events. Each pixel is modeled temporally. The experimental results on real data demonstrate the viability of the presented approach for processing neuromorphic sensor data across diverse real-world scenarios.