SIDAR: Synthetic Image Dataset for Alignment & Restoration

Monika Kwiatkowski, Simon Matern, Olaf Hellwich

2024

Abstract

In this paper, we present a synthetic dataset generation to create large-scale datasets for various image restoration and registration tasks. Illumination changes, shadows, occlusions, and perspective distortions are added to a given image using a 3D rendering pipeline. Each sequence contains the undistorted image, occlusion masks, and homographies. Although we provide two specific datasets, the data generation itself can be customized and used to generate an arbitrarily large dataset with an arbitrary combination of distortions. The datasets allow end-to-end training of deep learning methods for tasks such as image restoration, background subtraction, image matching, and homography estimation. We evaluate multiple image restoration methods to reconstruct the content from a sequence of distorted images. Additionally, a benchmark is provided that evaluates keypoint detectors and image matching methods. Our evaluations show that even learned image descriptors struggle to identify and match keypoints under varying lighting conditions.

Paper Citation

in Harvard Style

Kwiatkowski M., Matern S. and Hellwich O. (2024). SIDAR: Synthetic Image Dataset for Alignment & Restoration. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 175-189. DOI: 10.5220/0012391400003660

in Bibtex Style

@conference{visapp24,
author={Monika Kwiatkowski and Simon Matern and Olaf Hellwich},
title={SIDAR: Synthetic Image Dataset for Alignment & Restoration},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={175-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012391400003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - SIDAR: Synthetic Image Dataset for Alignment & Restoration
SN - 978-989-758-679-8
AU - Kwiatkowski M.
AU - Matern S.
AU - Hellwich O.
PY - 2024
SP - 175
EP - 189
DO - 10.5220/0012391400003660
PB - SciTePress