TY - INPR
A1 - Teuber, Tanja
A1 - Remmele, Steffen
A1 - Hesser, Jürgen
A1 - Steidl, Gabriele
T1 - Denoising by Higher Order Statistics
N2 - A standard approach for deducing a variational denoising method is the maximum a posteriori strategy. Here, the denoising result is chosen in such a way that it maximizes the conditional density function of the reconstruction given its observed noisy version. Unfortunately, this approach does not imply that the empirical distribution of the reconstructed noise components follows the statistics of the assumed noise model. In this paper, we propose to overcome this drawback by applying an additional transformation to the random vector modeling the noise. This transformation is then incorporated into the standard denoising approach and leads to a more sophisticated data fidelity term, which forces the removed noise components to have the desired statistical properties. The good properties of our new approach are demonstrated for additive Gaussian noise by numerical examples. Our method shows to be especially well suited for data containing high frequency structures, where other denoising methods which assume a certain smoothness of the signal cannot restore the small structures.
KW - denoising
KW - additive Gaussian noise
KW - maximum a posteriori estimation
KW - higher-order moments
Y1 - 2011
UR - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2765
UR - https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-27650
ER -