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 angular views. 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 views. 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. The performance is shown to be further improved using iterative back-projection as a post-processing step.

中文翻译：

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使用低秩先验和深度卷积神经网络的光场超分辨率


由于实用的光场捕获系统在计算机视觉领域提供了广泛的应用，光场成像最近重新引起了人们的兴趣。然而，捕获高分辨率光场在技术上仍然具有挑战性，因为角分辨率的增加通常伴随着空间分辨率的显着降低。本文描述了一种基于学习的空间光场超分辨率方法，该方法允许在所有角度视图上保持一致地恢复整个光场。该算法首先使用光流来对齐光场，然后使用低秩近似来减小其角度尺寸。然后，我们将所得低秩模型的线性独立列视为嵌入，并使用深度卷积神经网络（DCNN）进行恢复。然后使用超分辨率嵌入来重建剩余的视图。使用反向扭曲来恢复原始视差，其中使用新颖的光场修复算法来近似丢失的像素。实验结果表明，所提出的方法优于现有的光场超分辨率算法，与第二好的方法相比，PSNR 增益提高了 0.23 dB。使用迭代反投影作为后处理步骤可以进一步提高性能。