Abstract
This article presents an approach for noise filtering that is based on anisotropic nonlinear diffusion. The method combines edge-preserving noise reduction with a strategy to enhance local structures and a mechanism to further smooth the background. We have provided the method with an automatic mechanism for parameter self-tuning and for stopping the iterative filtering process. The performance of the approach is illustrated with its application to electron cryotomography (cryoET). CryoET has emerged as a leading imaging technique for visualizing the molecular architecture of complex biological specimens. A challenging computational task in this discipline is to increase the extremely low signal-to-noise ratio (SNR) to allow visualization and interpretation of the three-dimensional structures. The filtering method here proposed succeeds in substantially reducing the noise with excellent preservation of the structures.
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Fernández, J.J., Li, S., Lucic, V. (2007). Three-Dimensional Anisotropic Noise Reduction with Automated Parameter Tuning: Application to Electron Cryotomography. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_7
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DOI: https://doi.org/10.1007/978-3-540-75271-4_7
Publisher Name: Springer, Berlin, Heidelberg
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