Summary
Remote sensing puts high demands on image processing. It calls for state-of-the-art algorithms, e.g. neural networks. However, neural nets usually work on preprocessed data and the preprocessing steps themselves have proved difficult to implement with NNs. Here a NN-like paradigm for low-level image processing is presented, that is based on the evolution of coupled, non-linear diffusion equations. The illustrations are focussed on feature preserving noise reduction, but the framework is more general.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Proesmans, M., Van Gool, L.J., Vanroose, P. (1997). Non-Linear Diffusion as a Neuron-Like Paradigm for Low-Level Vision. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_19
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DOI: https://doi.org/10.1007/978-3-642-59041-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-63828-2
Online ISBN: 978-3-642-59041-2
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