A NEW APPROACH FOR DENOISING IMAGES BASED ON WEIGHTS OPTIMIZATION

Qiyu Jin, Ion Grama, Quansheng Liu

2012

Abstract

We propose a new algorithm to restore an image contaminated by the Gaussian white noise. Our approach is based on the weighted average of the observations in a neighborhood as in the case of the Non-Local Means Filter. But in contrast to the Non-Local Means Filter, we choose the weights by minimizing a tight upper bound of the Mean Square Error. Our theoretical results show that some ”oracle” weights defined by a triangular kernel are optimal. To construct a computable filter the ”oracle” weights are replaced by some estimates. The implementation of the proposed algorithm is straightforward. The simulations show that our approach is very competitive.

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Paper Citation


in Harvard Style

Jin Q., Grama I. and Liu Q. (2012). A NEW APPROACH FOR DENOISING IMAGES BASED ON WEIGHTS OPTIMIZATION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 112-117. DOI: 10.5220/0003846001120117


in Bibtex Style

@conference{visapp12,
author={Qiyu Jin and Ion Grama and Quansheng Liu},
title={A NEW APPROACH FOR DENOISING IMAGES BASED ON WEIGHTS OPTIMIZATION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={112-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003846001120117},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - A NEW APPROACH FOR DENOISING IMAGES BASED ON WEIGHTS OPTIMIZATION
SN - 978-989-8565-03-7
AU - Jin Q.
AU - Grama I.
AU - Liu Q.
PY - 2012
SP - 112
EP - 117
DO - 10.5220/0003846001120117