Blind multispectral image decomposition by 3D nonnegative tensor factorization
Optics Letters, Vol. 34, Issue 14, pp. 2210-2212 (2009)
http://dx.doi.org/10.1364/OL.34.002210
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Abstract
α-divergence-based nonnegative tensor factorization (NTF) is applied to blind multispectral image (MSI) decomposition. The matrix of spectral profiles and the matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models. NTF preserves local structure in the MSI that is lost as a result of vectorization of the image when nonnegative matrix factorization (NMF)- or independent component analysis (ICA)-based decompositions are used. Moreover, NTF based on the PARAFAC model is unique up to permutation and scale under mild conditions. To achieve this, NMF- and ICA-based factorizations, respectively, require enforcement of sparseness (orthogonality) and statistical independence constraints on the spatial distributions of the materials resident in the MSI, and these conditions do not hold. We demonstrate efficiency of the NTF-based factorization in relation to NMF- and ICA-based factorizations on blind decomposition of the experimental MSI with the known ground truth.
© 2009 Optical Society of America
OCIS Codes
(100.2960) Image processing : Image analysis
(100.3190) Image processing : Inverse problems
(100.6890) Image processing : Three-dimensional image processing
(150.6910) Machine vision : Three-dimensional sensing
(170.3880) Medical optics and biotechnology : Medical and biological imaging
ToC Category:
Image Processing
History
Original Manuscript: March 26, 2009
Revised Manuscript: June 19, 2009
Manuscript Accepted: June 21, 2009
Published: July 14, 2009
Virtual Issues
Vol. 4, Iss. 9 Virtual Journal for Biomedical Optics
Citation
Ivica Kopriva and Andrzej Cichocki, "Blind multispectral image decomposition by 3D nonnegative tensor factorization," Opt. Lett. 34, 2210-2212 (2009)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ol-34-14-2210
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