A Nonsmooth Graph-Based Approach to Light Field Super-Resolution

We propose a new super-resolution algorithm tailored for light field cameras, which suffer by design from a limited spatial resolution. In particular, we cast light field super-resolution into an optimization problem, where the particular structure of the light field data is captured by a nonsmooth graph-based regularizer, and where all the light field views are super-resolved jointly. Our experiments show that the proposed method compares favorably to the state-of-the-art light field super-resolution algorithms in terms of PSNR and visual quality. In particular, the nonsmooth graph-based regularizer leads to sharper images while preserving fine details.