Light field imaging has recently become a promising technology for 3D rendering and displaying. However, capturing real-world light field images still faces many challenges in both the quantity and quality. In this paper, we develop a learning based technique to reconstruct light field from a single 2D RGB image. It includes three steps: unsupervised monocular depth estimation, view synthesis and depth-guided view inpainting. We first propose a novel monocular depth estimation network to predict disparity maps of each sub-aperture views from the central view of light field. Then we synthesize the initial sub-aperture views by using the warping scheme. Considering that occlusion makes synthesis ambiguous for pixels invisible in the central view, we present a simple but effective fully convolutional network (FCN) for view inpainting. Note that the proposed network architecture is a general framework for light field reconstruction, which can be extended to take a sparse set of views as input without changing any structure or parameters of the network. Comparison experiments demonstrate that our method outperforms the state-of-the-art light field reconstruction methods with single-view input, and achieves comparable results with the multi-input methods.

光场成像最近已成为3D渲染和显示的有前途的技术。但是，捕获现实世界的光场图像在数量和质量上仍然面临许多挑战。在本文中，我们开发了一种基于学习的技术来从单个2D RGB图像重建光场。它包括三个步骤：无监督单眼深度估计，视图合成和深度引导视图修补。我们首先提出一种新颖的单眼深度估计网络，以从光场的中心视图预测每个子孔径视图的视差图。然后，我们使用变形方案合成初始子孔径视图。考虑到遮挡使像素在中央视图中不可见，因此合成模棱两可，因此我们提出了一种简单但有效的全卷积网络（FCN）用于视图修复。注意，提出的网络体系结构是用于光场重建的通用框架，可以扩展为将稀疏的视图集作为输入，而无需更改网络的任何结构或参数。对比实验表明，我们的方法优于单视图输入的最新光场重建方法，并且与多输入方法可比。