Variational Temporal Optical Flow for Multi-exposure Video

Onofre Martorell, Antoni Buades

2022

Abstract

High Dynamic Range (HDR) reconstruction for multi-exposure video sequences is a very challenging task. One of its main steps is registration of input frames. We propose a novel variational model for optical flow estimation for multi-exposure video sequences. We introduce data terms for consecutive and non consecutive frames, the latter term comparing frames with the same exposure. We also compute forward and backward flow terms for the current frame, naturally introducing temporal regularization. We present a particular formulation with sequences with two exposures, that can be extended to larger number of exposures. We compare the proposed method with state of the art variational models.

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Paper Citation


in Harvard Style

Martorell O. and Buades A. (2022). Variational Temporal Optical Flow for Multi-exposure Video. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 666-673. DOI: 10.5220/0010908300003124


in Bibtex Style

@conference{visapp22,
author={Onofre Martorell and Antoni Buades},
title={Variational Temporal Optical Flow for Multi-exposure Video},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={666-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010908300003124},
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 (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Variational Temporal Optical Flow for Multi-exposure Video
SN - 978-989-758-555-5
AU - Martorell O.
AU - Buades A.
PY - 2022
SP - 666
EP - 673
DO - 10.5220/0010908300003124
PB - SciTePress