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.
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Joint low-rank and sparse light field modelling for dense multiview data compression
Semantic Scholar · Computer Science · 2013
Abstract
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.