Accurate depth map estimation from a lenslet light field camera

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Light Field Image Processing: An Overview (2017, methodology): “[98] shared their code for light field depth estimation.”
Light field video capture using a learning-based hybrid imaging system (2017, unknown): “For the disparity CNN, we replace it with the model in (Kalantari et al. 2016), and two other traditional meth-ods (Jeon et al. 2015; Wang et al. 2015).”
Learning-based view synthesis for light field cameras (2016, unknown): “Since our disparity estimation CNN is trained to directly minimize the synthesis error, our estimated disparities are more suitable for this application than existing disparity estimation techniques [Wanner and Goldluecke 2012; Tao et al. 2013; Wang et al. 2015; Jeon et al. 2015] (see Fig.”
A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields (2016, background): “LF [5] poses depth estimation as a multilabel problem which is later refined by locally fitting a quadratic function.”
Learning a Deep Convolutional Network for Light-Field Image Super-Resolution (2015, methodology): “As stated in [14], a LF image with high spatial and angular resolution is preferred to obtain accurate correspondences.”
Soft 3D reconstruction for view synthesis (2017, background): “2015] and [Jeon et al. 2015]), we refer the reader to [Kalantari et al.”