用于光场重聚焦的机器学习方法
Machine learning method for light field refocusing
摘要：
光场成像引入了在捕获之后重新聚焦图像的能力。目前有两种流行的方法用于重聚焦，移位求和和傅立叶切片方法。这两种方法都不能在没有任何预处理的情况下实时地对光场进行重聚焦。本文介绍了一种基于机器学习的重聚焦技术，该技术能够提取16幅重聚焦图像，重聚焦参数为α= 0.125，0.250，0.375，.，2.0实时的。我们已经在两个实验中训练了我们的网络，称为RefNet。一旦使用傅立叶切片方法作为训练--即，“地面实况”-数据和另一个使用移位和求和方法作为训练数据。我们发现，在这两种情况下，RefNet方法不仅比以前的方法快至少134倍，而且RefNet的颜色预测优于傅立叶切片和移位求和方法，同时具有相似的景深和焦距性能。
英文摘要：
Light field imaging introduced the capability to refocus an image after capturing. Currently there are two popular methods for refocusing, shift-and-sum and Fourier slice methods. Neither of these two methods can refocus the light field in real-time without any pre-processing. In this paper we introduce a machine learning based refocusing technique that is capable of extracting 16 refocused images with refocusing parameters of α=0.125,0.250,0.375,...,2.0 in real-time. We have trained our network, which is called RefNet, in two experiments. Once using the Fourier slice method as the training -- i.e., "ground truth" -- data and another using the shift-and-sum method as the training data. We showed that in both cases, not only is the RefNet method at least 134x faster than previous approaches, but also the color prediction of RefNet is superior to both Fourier slice and shift-and-sum methods while having similar depth of field and focus distance performance.
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