IEEE Trans Image Process. 2018 Sep;27(9):4207-4218. doi: 10.1109/TIP.2018.2828983.
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.
光场相机通过单次曝光即可捕获场景中的 3D 信息。这一独特功能使其在多种应用中极具吸引力，例如后期重聚焦、深度估计和基于图像的渲染等。然而，光场相机的设计受到其空间分辨率的强烈限制。现成的超分辨率算法并不适用于光场数据，因为它们没有考虑到其结构。另一方面，少数专门针对光场数据的超分辨率算法则存在明显的局限性，例如需要进行昂贵的亚像素精度视差估计过程。我们提出了一种新的光场超分辨率算法，旨在解决这些限制。我们利用不同光场视图中的互补信息，一次性增强整个光场的空间分辨率。具体来说，我们表明，将多视图方法与基于图的正则化器相结合，可以避免需要精确且昂贵的视差估计步骤，同时保持光场的几何结构。大量实验表明，新算法在光场超分辨率的视觉质量和重建误差方面都优于最先进的方法。
