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Depth imaging denoising of photon-counting lidar
Applied Optics
|June 29, 2019
Summary
Photon-counting lidar struggles with noisy depth images. This study introduces a novel correlative photon and spatial correlation method to reduce noise, significantly improving image reconstruction accuracy compared to existing algorithms.
Area of Science:
- Optics and Photonics
- Computer Vision
- Signal Processing
Background:
- Photon-counting lidar systems are crucial for 3D imaging but are susceptible to ambient noise, hindering accurate depth reconstruction.
- Existing denoising algorithms for lidar depth imaging often fall short in effectively mitigating noise, impacting image quality and reliability.
Purpose of the Study:
- To develop a novel algorithm for photon-counting lidar systems that effectively reduces ambient noise.
- To improve the accuracy and quality of reconstructed target depth images in the presence of noise.
Main Methods:
- The proposed method utilizes correlative photons and spatial correlations to identify and reduce false alarm probabilities.
- This approach enhances the signal-to-noise ratio in photon-counting lidar data.
Main Results:
- The novel algorithm demonstrated a significant reduction in root mean square error for depth image reconstruction.
- Compared to the fast depth imaging denoising algorithm, the proposed method achieved 1.68 times better performance.
- The algorithm also outperformed log-matched filter estimation, showing 1.11 times improvement.
Conclusions:
- The proposed correlative photon and spatial correlation method effectively enhances the reconstructed images from photon-counting lidar.
- This advancement offers a promising solution for overcoming noise limitations in lidar depth imaging applications.
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