Lu Taoran, Qiu Su, Wang Hui, Zhu Shihao, Jin Weiqi
MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2024 Jun 15;24(12):3886. doi: 10.3390/s24123886.
In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the advent of new single-photon imaging devices, single-photon avalanche diode (SPAD) devices, with high sensitivity and a high depth resolution, have become cutting-edge research tools in the field of underwater imaging. However, the high production costs and small array areas of SPAD devices make it very difficult to conduct underwater SPAD imaging experiments. To address this issue, we propose a fast and effective underwater SPAD data simulation method and develop a denoising network for the removal of backward-scattering interference in underwater SPAD images based on deep learning and simulated data. The experimental results show that the distribution difference between the simulated and real underwater SPAD data is very small. Moreover, the algorithm based on deep learning and simulated data for the removal of backward-scattering interference in underwater SPAD images demonstrates effectiveness in terms of both metrics and human observation. The model yields improvements in metrics such as the PSNR, SSIM, and entropy of 5.59 dB, 9.03%, and 0.84, respectively, demonstrating its superior performance.
近年来，水下成像与视觉技术受到广泛关注，去除水中杂质引起的后向散射干扰已成为学者们长期的研究重点。随着新型单光子成像设备的出现，具有高灵敏度和高深度分辨率的单光子雪崩二极管（SPAD）设备成为水下成像领域的前沿研究工具。然而，SPAD设备的高生产成本和小阵列面积使得进行水下SPAD成像实验非常困难。为了解决这个问题，我们提出了一种快速有效的水下SPAD数据模拟方法，并基于深度学习和模拟数据开发了一种去噪网络，用于去除水下SPAD图像中的后向散射干扰。实验结果表明，模拟水下SPAD数据与真实数据之间的分布差异非常小。此外，基于深度学习和模拟数据的去除水下SPAD图像后向散射干扰的算法在指标和人工观察方面均显示出有效性。该模型在PSNR、SSIM和熵等指标上分别提高了5.59 dB、9.03%和0.84，显示出其优越的性能。
