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Article Dans Une Revue IEEE Journal of Selected Topics in Signal Processing Année : 2017

Super Resolution of Light Field Images using Linear Subspace Projection of Patch-Volumes

Résumé

Light field imaging has emerged as a very promising technology in the field of computational photography. Cameras are becoming commercially available for capturing real-world light fields. However, capturing high spatial resolution light fields remains technologically challenging, and the images rendered from real light fields have today a significantly lower spatial resolution compared to traditional 2D cameras. This paper describes an example-based super-resolution algorithm for light fields, which allows the increase of the spatial resolution of the different views in a consistent manner across all sub-aperture images of the light field. The algorithm learns linear projections between subspaces of reduced dimension in which reside patch-volumes extracted from the light field. The method is extended to cope with angular super-resolution, where 2D patches of intermediate sub-aperture images are approximated from neighbouring sub-aperture images using multivariate ridge regression. Experimental results show significant quality improvement when compared to state-of-the-art single-image super-resolution methods applied on each view separately, as well as when compared to a recent light field super-resolution techniques based on deep learning.
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Dates et versions

hal-01591488 , version 1 (21-09-2017)

Identifiants

Citer

Reuben A Farrugia, Christian Galea, Christine Guillemot. Super Resolution of Light Field Images using Linear Subspace Projection of Patch-Volumes. IEEE Journal of Selected Topics in Signal Processing, 2017, Special Issue on Light Field Image Processing, pp.1-14. ⟨10.1109/JSTSP.2017.2747127⟩. ⟨hal-01591488⟩
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