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Adaptive Smoothing via Contextual and Local Discontinuities
ABSTRACT
A novel adaptive smoothing approach is proposed for noise removal and
feature preservation where two distinct measures are simultaneously
adopted
to detect discontinuities in an image. Inhomogeneity underlying
an image is employed as a multi-scale measure to
detect contextual
discontinuities
for feature preservation and control of the smoothing speed,
while local spatial gradient is used for detection of
variable
local discontinuities during smoothing. Unlike previous adaptive smoothing
approaches, two discontinuity measures are combined
in our algorithm
for synergy in preserving non-trivial features,
which leads to a constrained anisotropic diffusion process that
inhomogeneity offers intrinsic constraints for selective
smoothing. Thanks to the use of intrinsic constraints,
our smoothing scheme is
insensitive to termination times and
the resultant images in a wide range of iterations are applicable to
achieve nearly identical results
for various early vision tasks.
Our algorithm is formally analyzed and related to anisotropic diffusion.
Comparative results indicate that
our algorithm yields favorable smoothing
results, and its application in extraction of hydrographic objects
demonstrates its usefulness as
a tool for early vision.
Click
tpami2005.pdf
for full text. Click Erratum
for correcting a typo in Eq.(9).