Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion

Christian Heinemann, Freddie Åström, George Baravdish, Kai Krajsek, Michael Felsberg, Hanno Scharr

2014

Abstract

In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types.

Paper Citation

in Harvard Style

Heinemann C., Åström F., Baravdish G., Krajsek K., Felsberg M. and Scharr H. (2014). Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 48-55. DOI: 10.5220/0004667500480055

in Bibtex Style

@conference{visapp14,
author={Christian Heinemann and Freddie Åström and George Baravdish and Kai Krajsek and Michael Felsberg and Hanno Scharr},
title={Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={48-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004667500480055},
isbn={978-989-758-003-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion
SN - 978-989-758-003-1
AU - Heinemann C.
AU - Åström F.
AU - Baravdish G.
AU - Krajsek K.
AU - Felsberg M.
AU - Scharr H.
PY - 2014
SP - 48
EP - 55
DO - 10.5220/0004667500480055