Noisy Image Processing Using the Independent Component Analysis Algorithm AMUSE
Salua Nassabay, Ingo R. Keck, Carlos G. Puntonet, Juan M. Górriz, J. Pérez de Inestroaa, Rubén M. Clemente
2007
Abstract
In this article we investigate the performance of the ICA algorithm AMUSE when applied to images contaminated by noise. The classes of noise we are using have gaussian, multiplicative and impulsive distributions. We find that AMUSE copes surprisingly well with the different types of noise, including multiplicative noise.
References
- Hyvärinen, A.: Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. In: Neural Computation. (1999) 1739-1768
- Hyvärinen, A., Hoyer, P., Oja, E.: Imagen denoising by sparse code shrinkage. In: Intelligent Signal Processing. (2001)
- Pajares Martin Sanz, G., De la Cruz García, J.: Visión por computador. Imágenes digitales y aplicaciones. RA-MA Editorial Madrid. (2001)
- Vhalupa, J.S.: The Visual Neurosciences. Werner editors. MIT Press (2003)
- Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Inter-science (2001)
- Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. nature. In: Vision Research. (1996) 381:607-609
- Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by v1? In: Vision Research. (1997) 37:3311-3325
- Bell, A., Sejnowski, T.: The independent component of natural scenes are adge filters. In: Vision Research. (1997) 3327-3338
- Hérault, J., Jutten, C., Ans, B.: Detection de grandeurs primitives dans un message composite par une architecture de calcul neuromimetique en apprentissage non supervise. In: X Colloque GRETSI. (1985) 1017-1022
- Jutten, C., Herault, J.: Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. In: Signal Processing. (1991) 24:1-10
- Comon, P.: Independent component analysis - a new concept. In: Signal Processing. (1994) 36:287-314
- Tong, L., Soon, V., Huang, Y., Liu, R.: Amuse: a new blind identification algorithm. In: Circuits and Systems, IEEE International Symposium on. Volume vol.3. (1990) 1784-1787
Paper Citation
in Harvard Style
Nassabay S., R. Keck I., G. Puntonet C., M. Górriz J., Pérez de Inestroaa J. and M. Clemente R. (2007). Noisy Image Processing Using the Independent Component Analysis Algorithm AMUSE . In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007) ISBN 978-972-8865-86-3, pages 83-90. DOI: 10.5220/0001635400830090
in Bibtex Style
@conference{anniip07,
author={Salua Nassabay and Ingo R. Keck and Carlos G. Puntonet and Juan M. Górriz and J. Pérez de Inestroaa and Rubén M. Clemente},
title={Noisy Image Processing Using the Independent Component Analysis Algorithm AMUSE},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)},
year={2007},
pages={83-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001635400830090},
isbn={978-972-8865-86-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)
TI - Noisy Image Processing Using the Independent Component Analysis Algorithm AMUSE
SN - 978-972-8865-86-3
AU - Nassabay S.
AU - R. Keck I.
AU - G. Puntonet C.
AU - M. Górriz J.
AU - Pérez de Inestroaa J.
AU - M. Clemente R.
PY - 2007
SP - 83
EP - 90
DO - 10.5220/0001635400830090