MULOG: A GENERIC VARIANCE-STABILIZATION APPROACH FOR SPECKLE REDUCTION IN SAR INTERFEROMETRY AND SAR POLARIMETRY
Abstract
Speckle reduction is a long-standing topic in SAR data pro-
cessing. Continuous progress made in the field of image
denoising fuels the development of methods dedicated to
speckle in SAR images. Adaptation of a denoising technique
to the specific statistical nature of speckle presents variable
levels of difficulty. It is well known that the logarithm trans-
form maps the intrinsically multiplicative speckle into an
additive and stationary component, thereby paving the way
to the application of general-purpose image denoising meth-
ods to SAR intensity images. Multi-channel SAR images
such as obtained in interferometric (InSAR) or polarimetric
(PolSAR) configurations are much more challenging. This
paper describes MuLoG, a generic approach for mapping
a multi-channel SAR image into real-valued images with
an additive speckle component that has a variance approxi-
mately constant. With this approach, general-purpose image
denoising algorithms can be readily applied to restore InSAR
or PolSAR data. In particular, we show how recent denois-
ing methods based on deep convolutional neural networks
lead to state-of-the art results when embedded with MuLoG
framework.