MAST: Mask-Accelerated Shearlet Transform for Densely-Sampled Light Field Reconstruction

Abstract

Shearlet Transform (ST) is one of the most effective algorithms for the Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF) with a large disparity range. However, ST requires a precise estimation of the disparity range of the SSLF in order to design a shearlet system with decent scales and to pre-shear the sparsely-sampled Epipolar-Plane Images (EPIs) of the SSLF. To overcome this limitation, a novel coarse-to-fine DSLF reconstruction method, referred to as Mask-Accelerated Shearlet Transform (MAST), is proposed in this paper. Specifically, a state-of-the-art learning-based optical flow method, FlowNet2, is employed to estimate the disparities of a SSLF. The estimated disparities are then utilized to roughly estimate the densely-sampled EPIs for the sparsely-sampled EPIs of the SSLF. Finally, an elaborately-designed soft mask for a coarsely-inpainted EPI is exploited to perform an iterative refinement on this EPI. Experimental results on nine challenging horizontal-parallax real-world SSLF datasets with large disparity ranges (up to 35 pixels) demonstrate the effectiveness and efficiency of the proposed method over the other state-of-the-art approaches.