A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging

TL;DR

This paper presents a novel noise-unmixing approach for photon-efficient LIDAR imaging that significantly improves depth and reflectivity estimation accuracy in high background noise conditions, enabling effective imaging at very low SBR levels.

Contribution

The paper introduces an adaptive noise-unmixing algorithm that enhances photon-efficient LIDAR imaging performance in low SBR scenarios, outperforming existing methods.

Findings

Effective at SBR as low as 0.04

Improved depth and reflectivity estimates over state-of-the-art

Demonstrated robustness across varying noise levels

Abstract

Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that focuses on unmixing contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, we borrow data from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content. Algorithm performance is demonstrated on experimental data at varying levels of noise. Results show improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low signal-to-background ratios. In particular, accurate imaging is demonstrated with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant method demonstrates the viability of rapid, long-range, and low-power LIDAR imaging.