Flexible Light Field Angular Superresolution via a Deep Coarse‐to‐Fine Framework
Qian Wang, Li Fang, Long Ye, Wei Zhong, Fei Hu, Qin Zhang · Wireless Communications and Mobile Computing · 2022
Acquisition of densely‐sampled light fields (LFs) is challenging. In this paper, we develop a coarse‐to‐fine light field angular superresolution that reconstructs densely‐sampled LFs from sparsely‐sampled ones. Unlike most of other methods, which are limited by the regularity of sampling patterns, our method can flexibly deal with different scale factors with one model. Specifically, a coarse restoration on epipolar plane images (EPIs) with arbitrary angular resolution is performed and then a refinement with 3D convolutional neural networks (CNNs) on stacked EPIs. The subaperture images in LFs are synthesized first horizontally, then vertically, forming a pseudo 4DCNN. In addition, our method can handle large baseline light field without using geometry information, which means it is not constrained by Lambertian assumption. Experimental results over various light field datasets including large baseline LFs demonstrate the significant superiority of our method when compared with state‐of‐the‐art ones.