One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measure, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets.

中文翻译：

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具有基于学习的匹配成本的光场图像的深度

光场成像的核心应用之一是深度估计。为了获取深度图，现有方法将单个光一致性测量应用于整个光场。但是，由于硬件设计的局限性导致光场退化不均匀，因此这不是最佳选择。在本文中，我们介绍了一种管道，该管道可自动确定用于光一致性测量的最佳配置，从而从光场获得最可靠的深度标签。我们分析了影响小透镜光场相机退化的实际因素，并设计了一个基于学习的框架，该框架可以检索最佳成本度量和最佳深度标签。为了提高我们方法的可靠性，我们增加了现有的光场基准，以模拟与实际光源有关的噪声，像差，和渐晕痕迹。增强的数据集用于训练和验证所提出的方法。对于基准和真实世界的光场数据集，我们的方法与几种最先进的方法相比具有竞争力。