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Light field (LF) images acquired by hand-held devices usually suffer from lowspatial resolution as the limited sampling resources have to be shared with theangular dimension. LF spatial super-resolution (SR) thus becomes anindispensable part of the LF camera processing pipeline. Thehigh-dimensionality characteristic and complex geometrical structure of LFimages make the problem more challenging than traditional single-image SR. Theperformance of existing methods is still limited as they fail to thoroughlyexplore the coherence among LF views and are insufficient in accuratelypreserving the parallax structure of the scene. In this paper, we propose anovel learning-based LF spatial SR framework, in which each view of an LF imageis first individually super-resolved by exploring the complementary informationamong views with combinatorial geometry embedding. For accurate preservation ofthe parallax structure among the reconstructed views, a regularization networktrained over a structure-aware loss function is subsequently appended toenforce correct parallax relationships over the intermediate estimation. Ourproposed approach is evaluated over datasets with a large number of testingimages including both synthetic and real-world scenes. Experimental resultsdemonstrate the advantage of our approach over state-of-the-art methods, i.e.,our method not only improves the average PSNR by more than 1.0 dB but alsopreserves more accurate parallax details, at a lower computational cost.

Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization