Abstract

In this paper, we take advantage of the clear texture structure of the epipolar plane image (EPI) in the light fifield data and model the problem of light fifield reconstruction from a sparse set of views as a CNN-based angular detail restoration on EPI. We indicate that one of the main challenges in sparsely sampled light fifield reconstruction is the information asymmetry between the spatial and angular domain, where the detail portion in the angular domain is damaged by undersampling. To balance the spatial and angular information, the spatial high frequency components of an EPI is removed using EPI blur, before feeding to the network. Finally, a non-blind deblur operation is used to recover the spatial detail suppressed by the EPI blur. We evaluate our approach on several datasets including synthetic scenes, real-world scenes and challenging microscope light fifield data. We demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms. We also show a further application for depth enhancement by using the reconstructed light fifield.