High-dimensional Guided Image Filtering

Shu Fujita, Norishige Fukushima

2016

Abstract

We present high-dimensional filtering for extending guided image filtering. Guided image filtering is one of edge-preserving filtering, and the computational time is constant to the size of the filtering kernel. The constant time property is essential for edge-preserving filtering. When the kernel radius is large, however, the guided image filtering suffers from noises because of violating a local linear model that is the key assumption in the guided image filtering. Unexpected noises and complex textures often violate the local linear model. Therefore, we propose high-dimensional guided image filtering to avoid the problems. Our experimental results show that our high-dimensional guided image filtering can work robustly and efficiently for various image processing.

Paper Citation

in Harvard Style

Fujita S. and Fukushima N. (2016). High-dimensional Guided Image Filtering . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 25-32. DOI: 10.5220/0005715100250032

in Bibtex Style

@conference{visapp16,
author={Shu Fujita and Norishige Fukushima},
title={High-dimensional Guided Image Filtering},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={25-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005715100250032},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - High-dimensional Guided Image Filtering
SN - 978-989-758-175-5
AU - Fujita S.
AU - Fukushima N.
PY - 2016
SP - 25
EP - 32
DO - 10.5220/0005715100250032