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Authors: Christian Heinemann 1 ; Freddie Åström 2 ; George Baravdish 2 ; Kai Krajsek 1 ; Michael Felsberg 2 and Hanno Scharr 1

Affiliations: 1 Forschungszentrum Jülich, Germany ; 2 Linköping University, Sweden

Keyword(s): Image Enhancement, Channel Representation, Channel Smoothing, Diffusion, Energy Minimization.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

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.

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Paper citation in several formats:
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 (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 48-55. DOI: 10.5220/0004667500480055

@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 (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={48-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004667500480055},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion
SN - 978-989-758-003-1
IS - 2184-4321
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
PB - SciTePress

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