  The image-based rendering approach using Shearlet Transform (ST) is one of
the state-of-the-art Densely-Sampled Light Field (DSLF) reconstruction methods.
It reconstructs Epipolar-Plane Images (EPIs) in image domain via an iterative
regularization algorithm restoring their coefficients in shearlet domain.
Consequently, the ST method tends to be slow because of the time spent on
domain transformations for dozens of iterations. To overcome this limitation,
this letter proposes a novel self-supervised DSLF reconstruction method,
CycleST, which applies ST and cycle consistency to DSLF reconstruction.
Specifically, CycleST is composed of an encoder-decoder network and a residual
learning strategy that restore the shearlet coefficients of densely-sampled
EPIs using EPI reconstruction and cycle consistency losses. Besides, CycleST is
a self-supervised approach that can be trained solely on Sparsely-Sampled Light
Fields (SSLFs) with small disparity ranges ($\leqslant$ 8 pixels). Experimental
results of DSLF reconstruction on SSLFs with large disparity ranges (16 - 32
pixels) from two challenging real-world light field datasets demonstrate the
effectiveness and efficiency of the proposed CycleST method. Furthermore,
CycleST achieves ~ 9x speedup over ST, at least.

Signal Processing;Multimedia;Image and Video Processing