Light field super-resolution using internal and external similarities

This paper presents a novel super-resolution method for light field images by jointly exploiting internal and external similarities. The internal similarity refers to the correlations that exist across the angular dimensions of the 4D light field itself, while the external similarity refers to the correlations learned from a conventional 2D image dataset. Our key observation is that the internal and external similarities are complementary to each other, and we propose a depth-adaptive fusion scheme to take advantage of both their merits. Moreover, we improve the traditional projection-based method that exploits the internal similarity, by introducing a back-projection refinement and getting rid of the dependency on camera parameters. Experimental results on a variety of light field images validate the superior performance of the proposed method.