DepthGuided View Synthesis for Light Field Reconstruction From a Single Image Abstract   Light field imaging has recently become a promising technology for 3D rendering and displaying. However, capturing realworld 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 depthguided view inpainting. We first propose a novel monocular depth estimation network to predict disparity maps of each subaperture views from the central view of light field. Then we synthesize the initial subaperture 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 stateoftheart light field reconstruction methods with singleview input, and achieves comparable results with the multiinput methods.

A 3D Convolutional Neural Network for Light Field Depth Estimation Depth estimation has always been a great challenge in the field of computer vision and machine learning. There is a rich literature focusing on depth estimation in stereo vision or in monocular imaging, while the domain of depth estimation in light field images is still in its infancy. The paper proposes a fully convolutional 3D neural network that estimates the disparity in light field images. The proposed method is parametric as it is able to adapt to input images of arbitrary size and it is lightweight and less prone to overfitting thanks to its fully convolutional nature. The experiments reveal competitive results against the state of the art, demonstrating the potential offered by deep learning solutions for disparity estimation in light field images.