Joint low-rank and sparse light field modelling for dense multiview data compression

The effective representation of the structures in the multiview images is an important problem that arises in visual sensor networks. This paper presents a novel recovery scheme from compressive samples which exploit local and non-local correlated structures in dense multiview images. The recovery model casts into convex minimization framework which penalizes the sparse and low-rank constraints on the data. The sparsity constraint models the correlations among pixels in a single image whereas the global correlations across images are modelled with the low-rank prior. Simulation results demonstrate that our approach achieves better reconstruct quality in comparison with the state-of-the-art reconstruction schemes.