We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensionality, we keep numerical complexity at bay by restricting the space of solutions and by exploiting an efficient Primal-Dual formulation. Comparisons with state of the art techniques, on both synthetic and real data, show promising performances.
CITATION STYLE
Kamal, M. H., Favaro, P., & Vandergheynst, P. (2015). A convex solution to disparity estimation from light fields via the primal-dual method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8932, pp. 350–363). Springer Verlag. https://doi.org/10.1007/978-3-319-14612-6_26
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