基于卷积神经网络的油藏深度预测 数字全息术

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摘要翻译：
数字全息术使我们能够从成像设备捕获的全息图中重建三维空间中的物体。对于重建，我们需要预先知道被重新编码对象的深度位置。在本研究中，我们提出使用卷积神经网络(CNN)为基础的回归进行深度预测。在以往的研究中，物体的深度是通过全息图不同深度位置的再现像来估计的，用一个表示最聚焦深度位置的度量；然而，这样的深度搜索是耗时的。该方法的CNN可以直接从全息图中预测出毫米级的深度位置。
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英文标题：
《Convolutional neural network-based regression for depth prediction in
  digital holography》
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作者：
Tomoyoshi Shimobaba, Takashi Kakue, Tomoyoshi Ito
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Digital holography enables us to reconstruct objects in three-dimensional space from holograms captured by an imaging device. For the reconstruction, we need to know the depth position of the recoded object in advance. In this study, we propose depth prediction using convolutional neural network (CNN)-based regression. In the previous researches, the depth of an object was estimated through reconstructed images at different depth positions from a hologram using a certain metric that indicates the most focused depth position; however, such a depth search is time-consuming. The CNN of the proposed method can directly predict the depth position with millimeter precision from holograms.
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PDF链接：
https://arxiv.org/pdf/1802.00664

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关键词：神经网络 神经网 Construction Mathematical Applications 对象 neural 聚焦 知道 holograms