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Robust photon-efficient imaging using a pixel-wise residual shrinkage network.

Gongxin Yao, Yiwei Chen, Yong Liu

|October 12, 2022

Summary

This summary is machine-generated.

This study introduces a novel network for photon-efficient imaging using single-photon light detection and ranging (LiDAR). The method enhances depth image accuracy from noisy data, outperforming existing techniques.

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Area of Science:

Photonics
Computer Vision
Signal Processing

Background:

Single-photon light detection and ranging (LiDAR) is crucial for 3D imaging in difficult conditions.
Limited photon counts and high noise in LiDAR data hinder precise depth image prediction.

Purpose of the Study:

To develop a photon-efficient imaging method for high-noise LiDAR data.
To improve the accuracy and confidence of depth estimation in challenging imaging scenarios.

Main Methods:

A pixel-wise residual shrinkage network was proposed for denoising intermediate features using adaptive soft thresholding.
The optimization target was redefined as pixel-wise classification for enhanced depth estimation.

Main Results:

The proposed model demonstrated superior performance compared to state-of-the-art methods on simulated and real-world datasets.
Robust imaging performance was maintained across various signal-to-noise ratios, including extreme cases (1:100).

Conclusions:

The pixel-wise residual shrinkage network effectively addresses noise in photon-limited LiDAR data.
The approach offers significant advantages for accurate and confident depth estimation in challenging 3D imaging applications.