@inproceedings{bf20d8f368bc410d9437e96324ceb1ea,
title = "Hyperspectral complex domain denoising",
abstract = "We consider hyperspectral complex domain imaging from hyperspectral complex-valued noisy observations. The proposed algorithm is based on singular value decomposition (SVD) of observations and complex domain block-matching 3D (CDBM3D) filtering in optimized SVD eigenspace. Simulation experiments demonstrate high efficiency of the proposed complex domain joint filtering of hyperspectral data in comparison with CDBM3D filtering of separate 2D slices of hyperspectral cubes as well as with respect to joint real domain independent phase/amplitude filtering this kind of data.",
keywords = "Hyperspectral imaging, singular value decomposition, sparse representation, noise filtering, noise in imaging systems",
author = "Vladimir Katkovnik and Igor Shevkunov and Karen Egiazarian",
year = "2019",
month = sep,
doi = "10.23919/EUSIPCO.2019.8903100",
language = "English",
isbn = "978-1-5386-7300-3",
series = "European Signal Processing Conference",
publisher = "IEEE",
booktitle = "2019 27th European Signal Processing Conference (EUSIPCO)",
address = "United States",
note = "European Signal Processing Conference ; Conference date: 01-01-1900",
}