Light Field Super-Resolution Using Edge-Preserved Graph-Based Regularization

Abstract

The light field information would be captured through light field cameras and in different directions regarding 3D image view recordings. In this paper, in order to increase the spatio-angular super-resolution quality and to decrease the reconstruction error regarding the light field information, we use a graph-based light field super-resolution strategy. Accordingly, in order to apply the complementary data in the light field views, we use a graph regularizer for the total recovery of the information and an edge-preserving technique that represents an isometry between curves in the 2D manifold and 5D space of the RGB image views. Moreover, the reconstruction of the light field information is based on applying the alternating direction method of multipliers (ADMM) algorithm. Accordingly, a recent enhanced ADMM model has been used in this paper which is denominated as “Plug-and-Play” and permits the user to plug an image reconstruction technique and a denoising methodology as the first and second sub-problems respectively. On that account, we would be able to resolve the light field super-resolution problem considering the graph-based light field structure as the first sub-problem and the edge-preserving technique as the denoising methodology. Consequently, by applying the proposed super-resolution strategy, the super-resolved light field outcome would be more favorable in terms of visual quality and reconstruction errors in comparison with other state-of-the-art methodologies.