Temporal Super-Resolution Using a Multi-Channel Illumination Source.

While sensing in high temporal resolution is necessary for a wide range of applications, it is still limited nowadays due to the camera sampling rate. In this work, we try to increase the temporal resolution beyond the Nyquist frequency, which is limited by the sensor's sampling rate. This work establishes a novel approach to temporal super-resolution that uses the object-reflecting properties from an active illumination source to go beyond this limit.

Illumination-Based Color Reconstruction for the Dynamic Vision Sensor.

This work demonstrates a novel, state-of-the-art method to reconstruct colored images via the dynamic vision sensor (DVS). The DVS is an image sensor that indicates only a binary change in brightness, with no information about the captured wavelength (color) or intensity level. However, the reconstruction of the scene's color could be essential for many tasks in computer vision and DVS.

Hardware analysis for motion estimation task.

This work introduces hardware metrics to evaluate imaging sensor (camera) ability to cope with temporal change (motion). Shifting from images towards moving elements demands better tools for evaluation than just refresh rate, and this work is here to close that gap. We focus on the sampling frequency, signal to noise ratio, rolling shutter, and modulation transfer function as a set of parameters to define four fundamental conditions to evaluate and compare the quality of motion sensing.