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。