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从单次曝光中利用光极化进行深度HDR成像.

Mara Pistellato1, Tehreem Fatima1, Michael Wimmer2

  • 1Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, 155, Via Torino, 30170 Venice, Italy.

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概括
此摘要是机器生成的。

这项研究引入了一种新的高动态范围 (HDR) 成像方法,使用单个极度度波器阵列 (PFA) 摄像头和外部偏振器. 与现有方法相比,该技术将HDR重建精度提高18%.

关键词:
在PFA摄像头上,PFA摄像头深度学习是一种深度学习.高动态范围成像高动态范围成像极对称成像技术的极对称成像技术

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科学领域:

  • 计算摄影摄影的使用
  • 图像处理 图像处理
  • 计算机视觉 计算机视觉 计算机视觉

背景情况:

  • 高动态范围 (HDR) 成像旨在捕捉比标准传感器更广泛的光强度范围.
  • 传统的HDR方法使用多次曝光和色调映射,而最近的研究则使用数据驱动模型或极度相机探索单次曝光技术.
  • 从单次曝光中估计HDR仍然具有挑战性,因为传感器动态范围的限制.

研究的目的:

  • 介绍一种新的HDR重建方法,使用单个极度过器阵列 (PFA) 摄像机与外部偏振器.
  • 将经典的HDR算法与对极度图像的数据驱动解决方案相结合.
  • 为了增强动态范围,并从单个捕获中准确地重建HDR场景.

主要方法:

  • 使用带有外部偏振器的PFA摄像头来增加动态范围并模拟各种曝光.
  • 开发了一个管道,将标准的HDR括号算法与对极度图像的数据驱动方法集成在一起.
  • 引入了两个卷积神经网络 (CNN) 模型:一个用于从PFA数据中估计场景属性,另一个用于改进音调映射.

主要成果:

  • 提出的方法证明了在合成和现实世界数据集上有效的HDR重建.
  • 在整个测试组中,达到23dB的峰值信号噪声比 (PSNR).
  • 在PSNR.中,超越最先进的方法的性能为18%.

结论:

  • 这种新的方法成功地利用极度度过器的光衰减来实现准确的HDR重建.
  • PFA成像,外部偏振和CNN模型的组合在单曝光HDR成像方面取得了重大进展.
  • 该方法为捕捉极端照明变化的场景提供了强大而有效的解决方案.