STORM: Super-resolving Transients by OveRsampled Measurements - Robotics Institute Carnegie Mellon University

STORM: Super-resolving Transients by OveRsampled Measurements

A. Pediredla, A. Raghuram, S. Narasimhan, I. Gkioulekas, and A. Veeraraghavan
Conference Paper, Proceedings of (ICCP) IEEE International Conference on Computational Photography, May, 2019

Abstract

Image sensors that can measure the time of travel of photons are gaining importance in a myriad of applications such as LIDAR, non-line of sight imaging, light-in-flight imaging, and imaging through scattering media. While the price of these sensors is dramatically shrinking, there remains a trade-off between spatial resolution and temporal resolution. While single-pixel detectors using the single photon avalanche diode (SPAD) technology can achieve 10-30 ps time resolution, the current generation array detectors can only produce an order of magnitude lower temporal resolution due to space-related fabrication constraints. Moreover, this limit is due to bandwidth, read-out and circuit-area constraints on the detector array and therefore unlikely to dramatically change in the next few years.In this paper, we demonstrate a computational imaging approach that utilizes multiple measurements with calibrated sub-temporal resolution delays on the illumination pulse and super-resolution post-processing algorithms that together can achieve an order of magnitude improvement in the time resolution of the acquired transients. We build an experimental prototype, using a 32 × 32 SPAD detector array with 400ps time resolution and demonstrate recovery of transients with ≈ 50ps time resolution, an 8× improvement in time resolution resulting in a 5× improvement in depth reconstruction error.

BibTeX

@conference{Pediredla-2019-113440,
author = {A. Pediredla and A. Raghuram and S. Narasimhan and I. Gkioulekas and A. Veeraraghavan},
title = {STORM: Super-resolving Transients by OveRsampled Measurements},
booktitle = {Proceedings of (ICCP) IEEE International Conference on Computational Photography},
year = {2019},
month = {May},
}