Fast Depth Imaging Denoising with the Temporal Correlation of Photons

TL;DR

This paper introduces a fast, noise-filtering method for LiDAR depth imaging that leverages the temporal correlation of photons to improve accuracy without increasing system complexity.

Contribution

It presents a novel approach combining Poisson statistics and temporal filtering to effectively reduce false alarms and enhance depth image accuracy in noisy conditions.

Findings

Accurately distinguishes close objects in high noise environments

Effectively filters false alarms using temporal photon correlation

Achieves fast depth imaging without added system complexity

Abstract

This paper proposes a novel method to filter out the false alarm of LiDAR system by using the temporal correlation of target reflected photons. Because of the inevitable noise, which is due to background light and dark counts of the detector, the depth imaging of LiDAR system exists a large estimation error. Our method combines the Poisson statistical model with the different distribution feature of signal and noise in the time axis. Due to selecting a proper threshold, our method can effectively filter out the false alarm of system and use the ToFs of detected signal photons to rebuild the depth image of the scene. The experimental results reveal that by our method it can fast distinguish the distance between two close objects, which is confused due to the high background noise, and acquire the accurate depth image of the scene. Our method need not increase the complexity of the system and is useful in power-limited depth imaging.