  A new denoising algorithm for hyperspectral complex domain data has been
developed and studied. This algorithm is based on the complex domain
block-matching 3D filter including the 3D Wiener filtering stage. The developed
algorithm is applied and tuned to work in the singular value decomposition
(SVD) eigenspace of reduced dimension. The accuracy and quantitative advantage
of the new algorithm are demonstrated in simulation tests and in the processing
of the experimental data. It is shown that the algorithm is effective and
provides reliable results even for highly noisy data.
