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Updated: Jun 22, 2025

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一种用于水下SPAD深度成像数据集的模拟方法.

Taoran Lu1, Su Qiu1, Hui Wang1

  • 1MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
概括
此摘要是机器生成的。

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研究人员开发了一种快速的水下SPAD数据模拟方法和一个深度学习的无线化网络,以消除水下图像的反向散射干扰. 这种方法有效地提高了图像质量,克服了当前单光子雪崩二极管 (SPAD) 成像技术的局限性.

科学领域:

  • 光学和光子学 在光学和光子学.
  • 计算机视觉 计算机视觉
  • 海洋技术 海洋技术

背景情况:

  • 水中杂质的反向散射干扰阻碍了水下成像质量.
  • 单光子雪崩二极管 (SPAD) 设备为先进的水下成像提供高灵敏度和深度分辨率.
  • 高成本和小数组尺寸的SPAD设备限制了实际的水下成像实验.

研究的目的:

  • 提出一种快速有效的水下SPAD数据模拟方法.
  • 开发基于深度学习的无线化网络,以消除向后散射干扰.
  • 验证模拟数据和深度学习对水下SPAD图像增强的有效性.

主要方法:

  • 开发了一种新的,快速的模拟方法,用于水下SPAD数据.
  • 在模拟的水下SPAD图像上创建了一个深度学习的denoising网络.
  • 通过使用定量指标 (PSNR,SSIM,) 和定性视觉评估评估了无声化性能.

主要成果:

  • 模拟的水下SPAD数据与现实世界的数据分布密切匹配.
  • 深度学习的无线化网络显著减少了向后散射干扰.
  • 在PSNR (+5.59 dB),SSIM (+9.03%) 和 (+0.84) 中取得了实质性的改进.
关键词:
这是一个SPAD摄像机.数据集数据集数据集模拟模拟是指一个模拟模拟.一个光子成像成像.水下成像技术的研究.

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结论:

  • 拟议的模拟方法为训练深度学习模型提供了可行的替代方案.
  • 深度学习有效地消除了水下SPAD图像中的反向散射干扰.
  • 这种方法提高了水下成像能力,克服了SPAD设备的局限性.