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Research on the Depth Image Reconstruction Algorithm Using the Two-Dimensional Kaniadakis Entropy Threshold.

Xianhui Yang1, Jianfeng Sun1,2, Le Ma1

  • 1National Key Laboratory of Laser Spatial Information, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, China.

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Summary
This summary is machine-generated.

This study introduces a novel 2D Kaniadakis entropy thresholding method to enhance photon-counting LiDAR imaging quality. The technique effectively denoises point cloud data, significantly improving target recovery in noisy environments.

Keywords:
Gm-APD LiDARKaniadakis entropypoint cloudtwo-dimensional histogram

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Area of Science:

  • Photon-counting LiDAR
  • Geiger-mode avalanche photon diode (Gm-APD) LiDAR
  • 3D imaging
  • Signal processing

Background:

  • Background noise in Gm-APD LiDAR systems degrades 3D imaging quality.
  • Weak signal extraction is crucial for effective point cloud data processing.
  • Existing methods struggle with low signal-to-background ratios (SBRs).

Purpose of the Study:

  • To propose a novel depth image estimation method for photon-counting LiDAR.
  • To address the limitations imposed by background noise on imaging quality.
  • To improve target recovery rates in low SBR conditions.

Main Methods:

  • A two-dimensional (2D) Kaniadakis entropy thresholding method is applied.
  • Weak signal extraction is reframed as a point cloud denoising problem.
  • Exploitation of signal peak aggregation and spatio-temporal correlation features in point cloud-intensity data.

Main Results:

  • The proposed method demonstrates effectiveness in low SBR environments.
  • Achieved a target recovery rate of 91.7% at an SBR of 0.025.
  • Outperforms existing methods like Peak-picking (15.7%), Cross-Correlation (7.0%), and SPIRAL (18.4%).
  • Reconstruction recovery ratio improved by 73.3% compared to SPIRAL.

Conclusions:

  • The 2D Kaniadakis entropy thresholding method significantly enhances target integrity in high-background-noise scenarios.
  • The approach provides a robust basis for feature extraction and target recognition in challenging LiDAR applications.
  • This method offers a substantial improvement over conventional techniques for noisy point cloud data.