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• W2895527865 abstract "Densely-sampled light fields (LFs) are beneficial to many applications such as depth inference and post-capture refocusing. However, it is costly and challenging to capture them. In this paper, we propose a learning based algorithm to reconstruct a densely-sampled LF fast and accurately from a sparsely-sampled LF in one forward pass. Our method uses computationally efficient convolutions to deeply characterize the high dimensional spatial-angular clues in a coarse-to-fine manner. Specifically, our end-to-end model first synthesizes a set of intermediate novel sub-aperture images (SAIs) by exploring the coarse characteristics of the sparsely-sampled LF input with spatial-angular alternating convolutions. Then, the synthesized intermediate novel SAIs are efficiently refined by further recovering the fine relations from all SAIs via guided residual learning and stride-2 4-D convolutions. Experimental results on extensive real-world and synthetic LF images show that our model can provide more than 3 dB advantage in reconstruction quality in average than the state-of-the-art methods while being computationally faster by a factor of 30. Besides, more accurate depth can be inferred from the reconstructed densely-sampled LFs by our method." @default.
• W2895527865 created "2018-10-12" @default.
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• W2895527865 date "2018-01-01" @default.
• W2895527865 modified "2023-10-16" @default.
• W2895527865 title "Fast Light Field Reconstruction with Deep Coarse-to-Fine Modeling of Spatial-Angular Clues" @default.
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• W2895527865 doi "https://doi.org/10.1007/978-3-030-01231-1_9" @default.
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