id: 06069768
dt: a
an: 06069768
au: Wetzler, Aaron; Kimmel, Ron
ti: Efficient Beltrami flow in patch-space.
so: Bruckstein, Alfred M. (ed.) et al., Scale space and variational methods in
    computer vision. Third international conference, SSVM 2011, Ein-Gedi,
    Israel, May 29‒June 2, 2011. Revised selected papers. Berlin:
    Springer (ISBN 978-3-642-24784-2/pbk). Lecture Notes in Computer
    Science 6667, 134-143 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: Beltrami flow; patch-space; denoising
ci: 
li: doi:10.1007/978-3-642-24785-9_12
ab: Summary: The Beltrami framework treats images as two dimensional manifolds
    embedded in a joint features-space domain. This way, a color image is
    considered to be a two dimensional surface embedded in a hybrid
    special-spectral five dimensional $\left\{ x,y,R,G,B\right\} $ space.
    Image selective smoothing, often referred to as a denoising filter,
    amounts to the process of area minimization of the image surface by
    mean curvature flow. One interesting variant of the Beltrami framework
    is treating local neighboring pixels as the feature-space. A distance
    is defined by the amount of deformation a local patch undergoes while
    traversing its support in the spatial domain. The question we try to
    tackle in this note is how to perform patch based denoising accurately,
    and efficiently. As a motivation we demonstrate the performance of the
    Beltrami filter in patch-space, and provide useful implementation
    considerations that allow for parameter tuning and efficient
    implementation on hand-held devices like smart phones.
rv: