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Depth-guided Learning Light Field Angular Super-resolution with Edge-aware Inpainting by Xia Liu; Minghui Wang; Anzhi Wang; Xiyao Hua; Shanshan Liu is a Computer Science article available to read on EtoBox.

What is Depth-guided Learning Light Field Angular Super-resolution with Edge-aware Inpainting about?

High angular resolution light field (LF) enables exciting applications such as depth estimation, virtual reality, and augmented reality. Although many light field angular super-resolution methods have been proposed, the reconstruction problem of LF with a wide-baseline is far from being solved. In this paper, we propose an end-to-end learning-based approach to achieve angular super-resolution of the light field with a wide-baseline. Our model consists of three components. We first train a convolutional neural network to predict the depth map for each sub-aperture view. Then the estimated depth maps are used to warp the input views. In the final component, we first use a convolutional neural network to fuse the initial warped light fields, and then we propose an edge-aware inpainting network to modify the inaccurate pixels in the near-edge regions. Accordingly, we design an EdgePyramid structure that contains multi-scale edges to perform the inpainting of near-edge pixels. Moreover, we introduce a novel loss function to reduce the artifacts and further estimate the similarity in near-edge regions. Experimental results on various light field datasets including large-baseline light fi

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Author

Xia Liu; Minghui Wang; Anzhi Wang; Xiyao Hua; Shanshan Liu

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)