  The Image-Based Rendering (IBR) approach using Shearlet Transform (ST) is one
of the most effective methods for Densely-Sampled Light Field (DSLF)
reconstruction. The ST-based DSLF reconstruction typically relies on an
iterative thresholding algorithm for Epipolar-Plane Image (EPI) sparse
regularization in shearlet domain, involving dozens of transformations between
image domain and shearlet domain, which are in general time-consuming. To
overcome this limitation, a novel learning-based ST approach, referred to as
Deep Residual Shearlet Transform (DRST), is proposed in this paper.
Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully
Convolutional Neural Network (CNN) to predict the residuals of the shearlet
coefficients in shearlet domain in order to reconstruct a densely-sampled EPI
in image domain. The DRST network is trained on synthetic Sparsely-Sampled
Light Field (SSLF) data only by leveraging elaborately-designed masks.
Experimental results on three challenging real-world light field evaluation
datasets with varying moderate disparity ranges (8 - 16 pixels) demonstrate the
superiority of the proposed learning-based DRST approach over the
non-learning-based ST method for DSLF reconstruction. Moreover, DRST provides a
2.4x speedup over ST, at least.
