Light field cameras 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 suffer by design from strong limitations in their spatial resolution. Off-the-shelf super-resolution algorithms are not ideal for light field data, as they do not consider its structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to carry out a costly disparity estimation procedure with sub-pixel precision. We propose a new light field super-resolution algorithm meant to address these limitations. We use the complementary information in the different light field views to augment the spatial resolution of the whole light field at once. In particular, we show that coupling the multi-view approach with a graph-based regularizer, which enforces the light field geometric structure, permits to avoid the need of a precise and costly disparity estimation step. Extensive experiments show that the new algorithm compares favorably to the state-of-the-art methods for light field super-resolution, both in terms of visual quality and in terms of reconstruction error.
We present a variational framework to generate super-resolved novel views from 4D light field data sampled at low resolution, for example by a plenoptic camera. In contrast to previous work, we formulate the problem of view synthesis as a continuous inverse problem, which allows us to correctly take into account foreshortening effects caused by scene geometry transformations. High-accuracy depth maps for the input views are locally estimated using epipolar plane image analysis, which yields floating point depth precision without the need for expensive matching cost minimization. The disparity maps are further improved by increasing angular resolution with synthesized intermediate views. Minimization of the super-resolution model energy is performed with state of the art convex optimization algorithms within seconds.