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Light field angular super-resolution by view-specific queries

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Abstract

Light field angular super-resolution (LFASR) aims to reconstruct the densely sampled light field from sparsely sampled inputs. Recently, convolutional neural network-based methods have achieved encouraging results. However, most of these approaches use view-specific characteristics of the target dense light (i.e., contents and view locations) separately, the geometry structure information of the target light field is not fully explored. To this end, we propose view-specific queries to integrate the view location information of the dense light field into Transformer (dubbed as ViewFormer) for LFASR. In particular, we first leverage a Transformer encoder to process the input sparsely sampled light field. Then, the view interpolation operation is used to process the extracted subaperture features along horizontal and vertical directions of the target light field, generating the new sub-aperture representations dubbed as view-specific queries. Next, the view-specific queries contain the view coordinate information of the target light field, and are dynamically enhanced by a Transformer decoder layer by layer. The enhanced view-specific queries are fed to the reconstruction module for final light field synthesis. Additionally, to further mine more information on input sparsely sampled light field, we employ the channel attention scheme to the building blocks of the Transformer. Extensive experiments are performed on commonly-used LFASR benchmarks. ViewFormer achieves new state-of-the-art results compared with other methods on popular LFASR benchmarks, including real-world and synthetic data.

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Data availability

The datasets generated or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. Note that the order of view interpolation does not affect the final LFASR results.

  2. We attempt to replace WMSA with the self-attention layer from ViT [39], but it consumes a significant amount of computational resources, making it infeasible to run ViewFormer on the experiment server. Additionally, we find that varying the window size in WMSA has minimal impact on the ViewFormer’s final performance, but it significantly affects the number of parameters and FLOPs. Considering the computational complexity, we ultimately choose to set the window size in WMSA to 4.

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Acknowledgements

This work is supported by the special projects in key areas of Guangdong Province (2022ZDZX1036) and Shenzhen Peacock Plan.

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Authors and Affiliations

  1. School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China

    Shunzhou Wang & Wei Gao

  2. Department of Engineering, Shenzhen MSU-BIT University, Shenzhen, 518172, China

    Yao Lu, Wang Xia, Peiqi Xia & Ziqi Wang

  3. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Shunzhou Wang, Yao Lu, Wang Xia, Peiqi Xia & Ziqi Wang

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  1. Shunzhou Wang
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Contributions

Shunzhou Wang: Conceptualization of this study, Methodology, Software, Writing. Yao Lu: Discussion, Review, Editing, Supervision. Wang Xia: Visualization, Validation, Review. Peiqi Xia: Visualization, Validation, Review. Ziqi Wang: Visualization, Validation, Review. Wei Gao: Discussion, Review, Editing.

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Wang, S., Lu, Y., Xia, W. et al. Light field angular super-resolution by view-specific queries. Vis Comput 41, 3565–3580 (2025). https://doi.org/10.1007/s00371-024-03620-y

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