Wang Hui, Qiu Su, Lu Taoran, Kuang Yanjin, Jin Weiqi
MOE Key Laboratory of Photoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2024 Jul 7;24(13):4401. doi: 10.3390/s24134401.
The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time-photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.
单光子雪崩二极管（SPAD）的高灵敏度和皮秒级时间分辨率能够提高水下探测系统的工作范围和成像精度。当使用水下SPAD成像系统检测目标时，水中粒子引起的后向散射常常导致重建的水下图像质量不佳。尽管诸如简单像素累积等方法已被证明对时间-光子直方图重建有效，但它们在高散射环境中的表现并不理想。因此，水下SPAD检测需要新的重建方法来获取高分辨率图像。在本文中，我们提出了一种算法，该算法通过采用K近邻（KNN）对多个目标和背景进行分类，从时间-光子直方图重建水下目标的高分辨率深度剖面。结果有助于像素累积和深度估计算法（如像素互相关和ManiPoP）的性能提升。我们使用公开实验数据集和水下模拟数据来验证所提算法的有效性。我们算法的结果表明，陆地目标和模拟水下目标的均方根误差（RMSE）分别降低了57.12%和23.45%，实现了高分辨率单光子深度剖面重建。
