Light field imaging has recently known a regain of interest due to the
availability of practical light field capturing systems that offer a wide range
of applications in the field of computer vision. However, capturing
high-resolution light fields remains technologically challenging since the
increase in angular resolution is often accompanied by a significant reduction
in spatial resolution. This paper describes a learning-based spatial light
field super-resolution method that allows the restoration of the entire light
field with consistency across all sub-aperture images. The algorithm first uses
optical flow to align the light field and then reduces its angular dimension
using low-rank approximation. We then consider the linearly independent columns
of the resulting low-rank model as an embedding, which is restored using a deep
convolutional neural network (DCNN). The super-resolved embedding is then used
to reconstruct the remaining sub-aperture images. The original disparities are
restored using inverse warping where missing pixels are approximated using a
novel light field inpainting algorithm. Experimental results show that the
proposed method outperforms existing light field super-resolution algorithms,
achieving PSNR gains of 0.23 dB over the second best performing method. This
performance can be further improved using iterative back-projection as a
post-processing step.

本文介绍了一种用于恢复整个光场并实现在所有子孔径图像上的一致性的学习空间光场超分辨率方法，其中算法先使用光流来对齐光场，然后使用低秩近似来减少其角度尺寸，最后通过 DCNN 来恢复超分辨率嵌入。实验结果表明，该方法优于现有的光场超分辨率算法，平均峰值信噪比相对于第二个最佳性能的方法提高了 0.23 dB; 并且还可以使用迭代反投影作为后处理步骤来进一步提高性能。