Graph-based light field super-resolution
Light field cameras can capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post capture refocus, to depth estimation and image-based rendering. However, light field cameras exhibit a very limited spatial resolution, which should therefore be increased by computational methods. Off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they ignore its particular structure. A few super-resolution algorithms explicitly devised for light field data exist, but they exhibit significant limitations, such as the need to carry out an explicit disparity estimation step for one or several light field views. In this work we present a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the information in the different light field views is used to augment the spatial resolution of the whole light field. In particular, we show that coupling the multi-frame paradigma with a graph regularizer that enforces the light field structure permits to avoid the costly and challenging disparity estimation step. Our experiments show that the proposed method compares favorably to the state-of-the-art for light field super-resolution algorithms, both in terms of PSNR and visual quality.