Acquiring Dynamic Light Fields Through Coded Aperture Camera
Acquiring Dynamic Light Fields Through Coded Aperture Camera
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DOI：
10.1007/978-3-030-58529-7_22
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发表时间：
2020
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影响因子：
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通讯作者：
Kohei Sakai;Keita Takahashi;T. Fujii;H. Nagahara
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作者：
Kohei Sakai;Keita Takahashi;T. Fujii;H. Nagahara
我们调查的问题，压缩采集的动态光场。用于压缩光场采集的有希望的解决方案是使用编码孔径相机，利用该编码孔径相机，可以从通过不同编码的孔径图案捕获的若干图像计算重建整个光场。该方法假设场景在整个采集过程中不发生移动，限制了真实的应用。然而，在这项研究中，我们假设目标场景可能会随着时间的推移而变化，并提出了一种使用编码孔径相机和卷积神经网络（CNN）获取动态光场（移动场景）的方法。为了成功处理场景运动，我们开发了一种新的图像观察配置，称为V形观察，并使用具有伪运动的动态光场数据集训练CNN。我们的方法是通过使用计算机生成的场景和一个真实的相机实验验证。
We investigate the problem of compressive acquisition of a dynamic light field. A promising solution for compressive light field acquisition is to use a coded aperture camera, with which an entire light field can be computationally reconstructed from several images captured through differently-coded aperture patterns. With this method, it was assumed that the scene should not move throughout the complete acquisition process, which restricted real applications. In this study, however, we assume that the target scene may change over time, and propose a method for acquiring a dynamic light field (a moving scene) using a coded aperture camera and a convolutional neural network (CNN). To successfully handle scene motions, we develop a new configuration of image observation, called V-shape observation, and train the CNN using a dynamic-light-field dataset with pseudo motions. Our method is validated through experiments using both a computer-generated scene and a real camera.