F. Tupin : A probabilistic
framework for patch-based estimation of noisy data and its application
to SAR imagery
In this presentation, a probabilistic framework for patch-based
non-local approaches will be described and applications in SAR imagery
presented.
First, an introduction to SAR imagery, its specificities and its
different modalities (amplitude, interferometry, polarimetry), will be
given. The statistical models existing for these data, which are highly
noisy due to the speckle phenomenon, will be presented.
Then, we will describe how patch-based non local approaches can be
formulated in a probabilitic framework. In this case, the problem is
seen as a weighted maximum likelihood estimation problem, and allows to
process any data for which a distribution model of the noise is
available. In the case of SAR imagery, a unified framework can be
successfully applied to amplitude images, interferometric data and
polarimetric data. This work has been done in collaboration with
Charles Deledalle and Loïc Denis.
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