Abstract
Total variation regularization and total variation flows (TVF) have been widely applied for image enhancement and denoising. To include a generic preservation of crossing curvilinear structures in TVF we lift images to the homogeneous space \(\mathbb {M}=\mathbb {R}^{d}\rtimes S^{d\!-\!1}\) of positions and orientations as a Lie group quotient in SE(d). For \(d=2\) this is called ‘total roto-translation variation’ by Chambolle & Pock. We extend this to \(d=3\), by a PDE-approach with a limiting procedure for which we prove convergence. We also include a Mean Curvature Flow (MCF) in our PDE model on \(\mathbb {M}\). This was first proposed for \(d=2\) by Citti et al. and we extend this to \(d=3\). Furthermore, for \(d=2\) we take advantage of locally optimal differential frames in invertible orientation scores (OS).
We apply our TVF and MCF in the denoising/enhancement of crossing fiber bundles in DW-MRI. In comparison to data-driven diffusions, we see a better preservation of bundle boundaries and angular sharpness in fiber orientation densities at crossings. We support this by error comparisons on a noisy DW-MRI phantom. We also apply our TVF and MCF in enhancement of crossing elongated structures in 2D images via OS, and compare the results to nonlinear diffusions (CED-OS) via OS.
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Duits, R., St-Onge, E., Portegies, J., Smets, B. (2019). Total Variation and Mean Curvature PDEs on the Space of Positions and Orientations. In: Lellmann, J., Burger, M., Modersitzki, J. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2019. Lecture Notes in Computer Science(), vol 11603. Springer, Cham. https://doi.org/10.1007/978-3-030-22368-7_17
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DOI: https://doi.org/10.1007/978-3-030-22368-7_17
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