@inproceedings{2aed2974cb3e40afb8a89d78e71d3381,
title = "Vector anisotropic filter for multispectral image denoising",
abstract = "In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.",
keywords = "Anisotropic filter, Multispectral image, Sparse matrix transform",
author = "{Ben Said}, Ahmed and Sebti Foufou and Rachid Hadjidj",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 12th International Conference on Quality Control by Artificial Vision ; Conference date: 03-06-2015 Through 05-06-2015",
year = "2015",
doi = "10.1117/12.2182746",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Fabrice Meriaudeau and Olivier Aubreton",
booktitle = "Twelfth International Conference on Quality Control by Artificial Vision",
}