Light field (LF) images acquired by hand-held devices usually suffer from low
spatial resolution as the limited sampling resources have to be shared with the
angular dimension. LF spatial super-resolution (SR) thus becomes an
indispensable part of the LF camera processing pipeline. The
high-dimensionality characteristic and complex geometrical structure of LF
images make the problem more challenging than traditional single-image SR. The
performance of existing methods is still limited as they fail to thoroughly
explore the coherence among LF views and are insufficient in accurately
preserving the parallax structure of the scene. In this paper, we propose a
novel learning-based LF spatial SR framework, in which each view of an LF image
is first individually super-resolved by exploring the complementary information
among views with combinatorial geometry embedding. For accurate preservation of
the parallax structure among the reconstructed views, a regularization network
trained over a structure-aware loss function is subsequently appended to
enforce correct parallax relationships over the intermediate estimation. Our
proposed approach is evaluated over datasets with a large number of testing
images including both synthetic and real-world scenes. Experimental results
demonstrate the advantage of our approach over state-of-the-art methods, i.e.,
our method not only improves the average PSNR by more than 1.0 dB but also
preserves more accurate parallax details, at a lower computational cost.

本文提出一种基于学习的轻场景图像空间超分辨率框架，在每个轻场景图像视角学习探索不同的信息，并通过正则化网络结构来确保场景的正确视差关系，实验结果表明该方法不仅提高了平均峰值信噪比超过 1.0 dB，而且以更低的计算成本保持了更准确的视差细节。

基于深度排列几何嵌入和结构一致性正则化的光场空间超分辨率

Light Field Spatial Super-resolution via Deep Combinatorial Geometry  Embedding and Structural Consistency Regularization

The acquisition of light field images with high angular resolution is costly.
Although many methods have been proposed to improve the angular resolution of a
sparsely-sampled light field, they always focus on the light field with a small
baseline, which is captured by a consumer light field camera. By making full
use of the intrinsic \textit{geometry} information of light fields, in this
paper we propose an end-to-end learning-based approach aiming at angularly
super-resolving a sparsely-sampled light field with a large baseline. Our model
consists of two learnable modules and a physically-based module. Specifically,
it includes a depth estimation module for explicitly modeling the scene
geometry, a physically-based warping for novel views synthesis, and a light
field blending module specifically designed for light field reconstruction.
Moreover, we introduce a novel loss function to promote the preservation of the
light field parallax structure. Experimental results over various light field
datasets including large baseline light field images demonstrate the
significant superiority of our method when compared with state-of-the-art ones,
i.e., our method improves the PSNR of the second best method up to 2 dB in
average, while saves the execution time 48$\times$. In addition, our method
preserves the light field parallax structure better.

本论文提出了一种基于端到端学习的方法，旨在通过充分利用光场的内在几何信息，对大基线光场进行角向超分辨率处理。实验证明，该方法与现有最先进的方法相比，平均提高了 2 dB 的峰值信噪比，并节省了 48 倍的执行时间，同时更好地保持光场视差结构。