Abstract
In this paper, we propose a new variational decomposition model which splits an image into two components: a first one containing the structure and a second one the texture or noise. Our decomposition model relies on the use of two semi-norms: the Besov semi-norm for the geometrical component, the negative Hilbert-Sobolev norms for the texture or noise. And the proposed model can be understood as generalizations of Daubechies-Teschke’s model and have been motivated also by Lorenz’s idea. And we illustrate our study with numerical examples for image decomposition and denoising.
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© 2007 Springer-Verlag Berlin Heidelberg
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Li, M., Feng, X. (2007). Variational Decomposition Model in Besov Spaces and Negative Hilbert-Sobolev Spaces. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_102
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DOI: https://doi.org/10.1007/978-3-540-74377-4_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74376-7
Online ISBN: 978-3-540-74377-4
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