Can We Use Neural Regularization to Solve Depth Super-resolution?

Milena Gazdieva, Oleg Voynov, Alexey Artemov, Youyi Zheng, Luiz Velho, Evgeny Burnaev

2022

Abstract

Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational problem statement with Tikhonov regularization where the regularizer is parametrized with a deep neural network. This approach was previously applied successfully in photoacoustic tomography. We experimentally show that its application to depth map super-resolution is difficult, and provide suggestions about the reasons for that.

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Paper Citation


in Harvard Style

Gazdieva M., Voynov O., Artemov A., Zheng Y., Velho L. and Burnaev E. (2022). Can We Use Neural Regularization to Solve Depth Super-resolution?. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 582-590. DOI: 10.5220/0010883500003124


in Bibtex Style

@conference{visapp22,
author={Milena Gazdieva and Oleg Voynov and Alexey Artemov and Youyi Zheng and Luiz Velho and Evgeny Burnaev},
title={Can We Use Neural Regularization to Solve Depth Super-resolution?},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={582-590},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010883500003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Can We Use Neural Regularization to Solve Depth Super-resolution?
SN - 978-989-758-555-5
AU - Gazdieva M.
AU - Voynov O.
AU - Artemov A.
AU - Zheng Y.
AU - Velho L.
AU - Burnaev E.
PY - 2022
SP - 582
EP - 590
DO - 10.5220/0010883500003124