Journal of Mathematical Imaging and Vision

, Volume 20, Issue 1–2, pp 147–162 | Cite as

Regularizing Flows for Constrained Matrix-Valued Images

  • C. Chefd'hotel
  • D. Tschumperlé
  • R. Deriche
  • O. Faugeras


Nonlinear diffusion equations are now widely used to restore and enhance images. They allow to eliminate noise and artifacts while preserving large global features, such as object contours. In this context, we propose a differential-geometric framework to define PDEs acting on some manifold constrained datasets. We consider the case of images taking value into matrix manifolds defined by orthogonal and spectral constraints. We directly incorporate the geometry and natural metric of the underlying configuration space (viewed as a Lie group or a homogeneous space) in the design of the corresponding flows. Our numerical implementation relies on structure-preserving integrators that respect intrinsically the constraints geometry. The efficiency and versatility of this approach are illustrated through the anisotropic smoothing of diffusion tensor volumes in medical imaging.

image regularization multi-valued data constraints lie groups homogeneous spaces Riemannian metric natural gradient structure-preserving algorithms 


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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • C. Chefd'hotel
    • 1
  • D. Tschumperlé
    • 2
  • R. Deriche
    • 3
  • O. Faugeras
    • 4
  1. 1.Odyssée Lab.INRIA Sophia-AntipolisSophia-AntipolisFrance
  2. 2.Odyssée Lab.INRIA Sophia-AntipolisSophia-AntipolisFrance
  3. 3.Odyssée Lab.INRIA Sophia-AntipolisSophia-AntipolisFrance
  4. 4.Odyssée Lab.INRIA Sophia-AntipolisSophia-AntipolisFrance

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