Sparse Decomposition as a Denoising Images Tool

Hatim Koraichi, Chakkor Otman

2021

Abstract

The sparse representation and Elimination of image noise has been largely used successfully by the signal processing community. In this work, we present its benefits particularly in image denoising applications. The general purpose of sparse representation of data is to find the best approximation of a target signal applying a linear combination of a few elementary signals from a fixed collection. Several methods have been found for sparse decompositions to remove noise from the image, and there are other problems, like How to decompose a signal with a dictionary, which dictionary to use, and learning the dictionary.

Paper Citation

in Harvard Style

Koraichi H. and Otman C. (2021). Sparse Decomposition as a Denoising Images Tool. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 440-443. DOI: 10.5220/0010736100003101

in Bibtex Style

@conference{bml21,
author={Hatim Koraichi and Chakkor Otman},
title={Sparse Decomposition as a Denoising Images Tool},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={440-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010736100003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Sparse Decomposition as a Denoising Images Tool
SN - 978-989-758-559-3
AU - Koraichi H.
AU - Otman C.
PY - 2021
SP - 440
EP - 443
DO - 10.5220/0010736100003101