Abstract
Many image processing and computer vision applications require a preprocessing of the image to remove or reduce noise. Gaussian noise is a challenging type of noise whose removal has led to the proposal of several noise filters. In this paper we present a novel version of the morphological filters based on amoebas with the aim to incorporate fuzzy logic into them to achieve a better treatment of the uncertainty. The experimental results show that the proposed algorithm outperforms the classical amoeba-based filters both from the visual point of view and the quantitative performance values for images corrupted with Gaussian noise with standard deviation from 10 to 30.
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Notes
- 1.
This image database can be downloaded from http://sipi.usc.edu/database/misc.tar.gz.
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This paper has been partially supported by the Spanish grant TIN2013-42795-P.
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González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D. (2016). Gaussian Noise Reduction Using Fuzzy Morphological Amoebas. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-40596-4_55
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