Underwater 4D imaging quality enhancement of streak tube imaging LiDAR at extremely low SNR
Streak tube imaging LiDAR (STIL) has garnered significant attention in underwater detection due to its excellent imaging resolution and range gating capability. However, in highly attenuating and complex underwater environments, the STIL systems often encounter issues of system blur and low signal-to-noise ratio. To address these challenges, this study proposes a joint denoising and deblurring method based on the alternating direction method of multipliers framework. The feasibility and robustness of the method were verified through experiments conducted under different water quality conditions. The underwater 4D (3D point cloud + reflectance) imaging experiments of a hand model and a mannequin model at different depths of field indicate that the intricate detail of the image can be successfully restored even in extremely weak echo scenarios. Our results clearly demonstrate that the proposed method not only effectively eliminates noise and blur from underwater streak images but also precisely determines the position of the echo signal by utilizing the spatial correlation. This research significantly boosts the recognition capability of the STIL system in underwater imaging, thus highlighting its ability to acquire 4D information of weak echo signal targets in various complex underwater environments.
Authors
- Y. Z. Xue (ORCID: https://orcid.org/0009-0009-6546-1717)
- Jinshou Tian (ORCID: https://orcid.org/0000-0001-9810-1093)
- KAI QIAO
- Fei Yin (ORCID: https://orcid.org/0000-0001-6758-6884)
- M.-C. Fang (ORCID: https://orcid.org/0009-0007-9732-7593)
- Xing Wang (ORCID: https://orcid.org/0000-0003-3955-0686)
Institutions
- Chinese Academy of Sciences (CN)
- Shanxi University of Traditional Chinese Medicine (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Applied Optics
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1364/ao.558785
- Citations
- 3
- Primary Topic
- Advanced Optical Sensing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 3.58