Curvature driven image denoising algorithm. Iso-brightness contours in the grayscale input image are viewed as a level set. The level set is then evolved using a curvature-based speed function.
The advantage of this approach is that sharp boundaries are preserved with smoothing occurring only within a region. However, it should be noted that continuous application of this scheme will result in the eventual removal of all information as each contour shrinks to zero and disappear.
This filter has two parameters: the number of update iterations to be performed and the timestep between each update. The timestep should be “small enough” to ensure numerical stability. Stability is guarantee when the timestep meets the CFL (Courant-Friedrichs-Levy) condition. Broadly speaking, this condition ensures that each contour does not move more than one grid position at each timestep. In the literature, the timestep is typically user specified and have to manually tuned to the application.
Type: Image4DFloat, Required, Single
Time Step Number
Set the timestep parameter.
Set the number of iterations.
- “Level Set Methods and Fast Marching Methods”, J.A.Sethian, Cambridge Press, Chapter 16, Second edition, 1999.
- “The Insight Segmentation and Registration Toolkit” www.itk.org
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