Light Field Super-Resolution via LFBM5D Sparse Coding
Martin Alain, Aljoša Smolić · 2018
In this paper, we propose a spatial super-resolution method for light fields, which combines the SR-BM3D single image super-resolution filter and the recently introduced LFBM5D light field denoising filter. The proposed algorithm iteratively alternates between an LFBM5D filtering step and a back-projection step. The LFBM5D filter creates disparity compensated 4D patches which are then stacked together with similar 4D patches along a 5thdimension. The 5D patches are then filtered in the 5D transform domain to enforce a sparse coding of the high-resolution light field, which is a powerful prior to solve the ill-posed super-resolution problem. The back-projection step then impose the consistency between the known low-resolution light field and the-high resolution estimate. We further improve this step by using image guided filtering to remove ringing artifacts. Results show that significant improvement can be achieved compared to state-of-the-art methods, for both light fields captured with a lenslet camera or a gantry.