<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper deals with the nonlinear tensor diffusion which yields a coherence improvement. It is very appropriate for images with flow-like structures. Two convolutions are used in the construction of diffusion tensor for such a model, see [Drblíková, O.—Mikula, K.: <italic>Convergence analysis of finite volume scheme for nonlinear tensor anisotropic diffusion in image processing,</italic> SIAM J. Numer. Anal. <bold>46</bold> (2007), 37–60], [Weickert, J.: <italic>Coherence-enhancing diffusion filtering,</italic> Int. J. Comput. Vis. <bold>31</bold> (1999), 111–127]. In this paper we introduce the third supplemental convolution in order to enhance the diffusion strategy.</p><p>First, we briefly present the classical coherence enhancing model and explain our modification. Then the discrete scheme is provided. The core of the paper consists in numerical experiments. Benefits of the additional convolution are discussed and illustrated in the figures.</p></abstract>

The paper deals with the nonlinear tensor diffusion which yields a coherence improvement. It is very appropriate for images with flow-like structures. Two convolutions are used in the construction of diffusion tensor for such a model, see [Drblíková, O.—Mikula, K.: Convergence analysis of finite volume scheme for nonlinear tensor anisotropic diffusion in image processing, SIAM J. Numer. Anal. 46 (2007), 37–60], [Weickert, J.: Coherence-enhancing diffusion filtering, Int. J. Comput. Vis. 31 (1999), 111–127]. In this paper we introduce the third supplemental convolution in order to enhance the diffusion strategy.First, we briefly present the classical coherence enhancing model and explain our modification. Then the discrete scheme is provided. The core of the paper consists in numerical experiments. Benefits of the additional convolution are discussed and illustrated in the figures.

Regularized Coherence Enhancing Filtering

Tatra Mountains Mathematical Publications

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Regularized Coherence Enhancing Filtering"}

The paper deals with the nonlinear tensor diffusion which yields a coherence improvement. It is very appropriate for images with flow-like structures. Two...

Regularized Coherence Enhancing Filtering

Online veröffentlicht: 12. März 2019

Seitenbereich: 107 - 121

Eingereicht: 26. Sept. 2018

DOI: https://doi.org/10.2478/tmmp-2018-0024

Schlüsselwörter
image processing, nonlinear tensor diffusion, coherence enhancing filtering, semiimplicit scheme, diamond-cell finite volume method, convolution, regularization

© 2018 Ol’ga Stašová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Regularized Coherence Enhancing Filtering

Online veröffentlicht: 12. März 2019

Seitenbereich: 107 - 121

Eingereicht: 26. Sept. 2018

DOI: https://doi.org/10.2478/tmmp-2018-0024

Schlüsselwörterimage processing, nonlinear tensor diffusion, coherence enhancing filtering, semiimplicit scheme, diamond-cell finite volume method, convolution, regularization

© 2018 Ol’ga Stašová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
image processing, nonlinear tensor diffusion, coherence enhancing filtering, semiimplicit scheme, diamond-cell finite volume method, convolution, regularization