A Simulation Method for Underwater SPAD Depth Imaging Datasets

Taoran Lu, Su Qiu*, Hui Wang, Shihao Zhu, Weiqi Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number3886
JournalSensors
Volume24
Issue number12
DOIs
Publication statusPublished - Jun 2024

Keywords

  • SPAD camera
  • dataset
  • simulation
  • single-photon imaging
  • underwater imaging

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