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Hyperspectral complex domain denoising

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2019 27th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-9-0827-9703-9
ISBN (Print)978-1-5386-7300-3
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Period1/01/00 → …


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.


  • Hyperspectral imaging, singular value decomposition, sparse representation, noise filtering, noise in imaging systems

Publication forum classification

Field of science, Statistics Finland