Light field cameras can capture the 3D information in a scene with a single shot. This special feature makes light field cameras very appealing for a variety of applications: from the popular post-capture refocus, to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution, which should therefore be augmented by computational methods. On the one hand, off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they do not consider its particular structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to estimate an explicit disparity map at each view. In this work we propose a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field. We show that coupling the multi-frame approach with a graph regularizer, that enforces the light field structure via non local self similarities, permits to avoid the costly and challenging disparity estimation step for all the views. Extensive experiments show that the proposed algorithm compares favorably to the other state-of-the-art methods for light field super-resolution, both in terms of PSNR and in terms of visual quality. Moreover, differently from the other light field super-resolution methods, the new algorithm provides reconstructed light field views with uniform quality, which happens to be an important feature for any light field application.

本研究提出了一种新的光场超分辨率算法，采用多帧方法，使用不同光场视图中的信息来增强整个光场的空间分辨率，结合图形正则化器通过非局部自相似性来避免各个视图昂贵且具挑战性的差异估计步骤。实验表明，本算法在PSNR和视觉质量方面都与其他光场超分辨率最先进方法相比优越。

基于图形正则化的光场超分辨率