Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency

Abstract

Shearlet Transform (ST) has been instrumental for the Densely-Sampled Light Field (DSLF) reconstruction, as it sparsifies the underlying Epipolar-Plane Images (EPIs). The sought sparsification is implemented through an iterative regularization, which 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 employs ST and cycle consistency. 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. CycleST is a self-supervised approach that can be trained solely on Sparsely-Sampled Light Fields (SSLFs) with small disparity ranges (⩽ 8 pixels). Experimental results of DSLF reconstruction on SSLFs with large disparity ranges (16-32 pixels) demonstrate the effectiveness and efficiency of the proposed CycleST method. Furthermore, CycleST achieves ∼ 9x speedup over ST, at least.