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.

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