Computational neuromorphic imaging: principles and applications

Abstract

The widespread presence and use of visual data highlight the fact that conventional frame-based electronic sensors may not be well-suited for specific situations. For instance, in many biomedical applications, there is a need to image dynamic specimens at high speeds, even though these objects occupy only a small fraction of the pixels within the entire field of view. Consequently, despite capturing them at a high frame rate, many resulting pixel values are uninformative and therefore discarded during subsequent computations. Neuromorphic imaging, which makes use of an event sensor that responds to changes in pixel intensities, is ideally suitable for detecting such fast-moving objects. In this work, we outline the principle of such detectors, demonstrate their use in a computational imaging setting, and discuss the computational algorithms to process such event data for a variety of applications.