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Authors: Cesario Vincenzo Angelino ; Eric Debreuve and Michel Barlaud

Affiliation: Laboratory I3S University of Nice-Sophia Antipolis/CNRS, France

Keyword(s): Image denoising,variational methods, entropy, k-th nearest neighbors.

Abstract: In this paper we address the image restoration problem in the variational framework. The focus is set on denoising applications. Natural image statistics are consistent with a Markov random field (MRF) model for the image structure. Thus in a restoration process attention must be paid to the spatial correlation between adjacent pixels.The proposed approach minimizes the conditional entropy of a pixel knowing its neighborhood. The estimation procedure of statistical properties of the image is carried out in a new adaptive weighted k-th nearest neighbor (AWkNN) framework. Experimental results show the interest of such an approach. Restoration quality is evaluated by means of the RMSE measure and the SSIM index, more adapted to the human visual system.

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Paper citation in several formats:
Vincenzo Angelino, C.; Debreuve, E. and Barlaud, M. (2008). A MINIMUM ENTROPY IMAGE DENOISING ALGORITHM - Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising. In Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: BAIPCV; ISBN 978-989-8111-21-0; ISSN 2184-4321, SciTePress, pages 577-582. DOI: 10.5220/0001092605770582

@conference{baipcv08,
author={Cesario {Vincenzo Angelino}. and Eric Debreuve. and Michel Barlaud.},
title={A MINIMUM ENTROPY IMAGE DENOISING ALGORITHM - Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: BAIPCV},
year={2008},
pages={577-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001092605770582},
isbn={978-989-8111-21-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: BAIPCV
TI - A MINIMUM ENTROPY IMAGE DENOISING ALGORITHM - Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising
SN - 978-989-8111-21-0
IS - 2184-4321
AU - Vincenzo Angelino, C.
AU - Debreuve, E.
AU - Barlaud, M.
PY - 2008
SP - 577
EP - 582
DO - 10.5220/0001092605770582
PB - SciTePress

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