TY - JOUR A2 - Lee, Ivan AU - Wang, Qian AU - Fang, Li AU - Ye, Long AU - Zhong, Wei AU - Hu, Fei AU - Zhang, Qin PY - 2022 DA - 2022/03/10 TI - Flexible Light Field Angular Superresolution via a Deep Coarse-to-Fine Framework SP - 4570755 VL - 2022 AB - 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. SN - 1530-8669 UR - https://doi.org/10.1155/2022/4570755 DO - 10.1155/2022/4570755 JF - Wireless Communications and Mobile Computing PB - Hindawi KW - ER -