Disparity Enhancement-Based Light Field Angular Super-Resolution

Dongjun Cai, Yilei Chen, Xinpeng Huang, Ping An

Abstract: The depth-dependent light field (LF) reconstruction is a prevalent solution for large disparity LFs, which first estimates disparity maps and subsequently interpolates the content of target views by warping input views. However, replication errors often occur in edge regions of objects owing to inappropriate sampling positions caused by occlusion during disparity-based warping. Thus, we propose a disparity enhancement network that utilizes morphological filtering to address this distortion, which can adaptively modify disparity values in edge regions to obtain proper sampling positions. In addition, we develop an effective detail recovery network to mitigate interpolation errors introduced by inaccurate disparity estimation or warping operations. Experiments demonstrate that our approach significantly surpasses current state-of-the-art methods in large disparity LFs.