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Fast Light Field Reconstruction with Deep Coarse-to-Fine Modeling of Spatial-Angular Clues

  • Henry Wing Fung Yeung
  • Junhui HouEmail author
  • Jie Chen
  • Yuk Ying Chung
  • Xiaoming Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11210)

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.

Keywords

Light field Deep learning Convolutional neural network Super resolution View synthesis 

Notes

Acknowledgements

This work was supported in part by the CityU Start-up Grant for New Faculty under Grant 7200537/CS and in part by the Hong Kong RGC Early Career Scheme Funds 9048123 (CityU 21211518).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Henry Wing Fung Yeung
    • 1
  • Junhui Hou
    • 2
    Email author
  • Jie Chen
    • 3
  • Yuk Ying Chung
    • 1
  • Xiaoming Chen
    • 4
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.Department of Computer ScienceCity University of Hong KongKowloonHong Kong
  3. 3.School of Electrical and Electronics EngineeringNanyang Technological UniversitySingaporeSingapore
  4. 4.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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