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Automatic detection of filters in images with Gaussian noise using independent component analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2007) 4507 LNCS 692-699
DOI: 10.1007/978-3-540-73007-1_83
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Abstract

In this article we present the results of a study carried out using the popular fastica algorithm applied to the detection of filters in natural images in gray-scale, contaminated with gaussian noise. The detection of filters has been accomplished by using the statistical distribution measures kurtosis and skewness. © Springer-Verlag Berlin Heidelberg 2007.

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Nassabay, S., Keck, I. R., Puntonet, C. G., Clemente, R. M., & Lang, E. W. (2007). Automatic detection of filters in images with Gaussian noise using independent component analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 692–699). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_83

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