Half Gaussian Kernels Based Shock Filter for Image Deblurring and Regularization

Baptiste Magnier, Huanyu Xu, Philippe Montesinos

Abstract

In this paper, a shock-diffusion model is presented to restore both blurred and noisy image. The proposed approach uses a half smoothing kernel to get the precise edge directions, and use different shock-diffusion strategies for different image regions. Experiment results on real images show that the proposed model can effectively eliminate noise and enhance edges while preserving small objects and corners simultaneously. Compared to other approaches, the proposed method offers both better visual results and qualitative measurements.

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Paper Citation


in Harvard Style

Magnier B., Xu H. and Montesinos P. (2013). Half Gaussian Kernels Based Shock Filter for Image Deblurring and Regularization . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 51-60. DOI: 10.5220/0004224500510060


in Bibtex Style

@conference{visapp13,
author={Baptiste Magnier and Huanyu Xu and Philippe Montesinos},
title={Half Gaussian Kernels Based Shock Filter for Image Deblurring and Regularization},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={51-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004224500510060},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Half Gaussian Kernels Based Shock Filter for Image Deblurring and Regularization
SN - 978-989-8565-47-1
AU - Magnier B.
AU - Xu H.
AU - Montesinos P.
PY - 2013
SP - 51
EP - 60
DO - 10.5220/0004224500510060