Posterior Expectation of the Total Variation Model
SIAM Journal on Imaging Sciences(2013)
摘要
The total variation image (or signal) denoising model is a variational approach
that can be interpreted, in a Bayesian framework, as a search for the maximum
point of the posterior density (maximum a posteriori estimator). This maximization
aspect is partly responsible for a restoration bias called the “staircasing effect,” that is, the outbreak of quasi-constant regions separated by sharp edges in the
intensity map. In this paper we study a variant of this model that considers the
expectation of the posterior distribution instead of its maximum point. Apart
from the least square error optimality, this variant seems to better account
for the global properties of the posterior distribution. We present theoretical and numerical
results that demonstrate in particular that images denoised with
this model do not suffer from the staircasing effect.
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关键词
image denoising,total variation,Bayesian model,least square estimate,maximum a posteriori,estimation in high-dimensional spaces,proximity operators,staircasing effect,68U10,62H35
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