This paper introduces the first self-supervised neuromorphic super-resolution prototype. It can be self-adaptive to per input source from any low-resolution camera to estimate an optimal, high-resolution counterpart of any scale, without the need of side knowledge and prior training.
This paper presents the implementation of time-resolved velocity profile measurement using event-based vision
(EBV) employing an event-camera in-place of a high-speed camera.
The Prophesee GenX320 camera module brings event-based vision sensing to the OpenMV Cam platform. Inspired by the human retina, each sensor pixel responds independently and asynchronously to relative changes in illumination leading to efficient, high-speed and low power vision sensing.
In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. It proposes four algorithms based on the matrix of infinite impulse response (IIR) filters method.
This paper investigates event-based shape from polarization using Spiking Neural Networks (SNNs), introducing the Single-Timestep and Multi-Timestep Spiking UNets for effective and efficient surface normal estimation.