Open AccessNon‐negative matrix factorisation for blind source separation in wavelet transform domain
Author(s) -
Hattay Jamel,
Belaid Samir,
Naanaa Wady
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0409
Subject(s) - matrix decomposition , blind signal separation , non negative matrix factorization , artificial intelligence , mathematics , factorization , algorithm , wavelet , wavelet transform , matrix (chemical analysis) , decomposition , inverse , pattern recognition (psychology) , image (mathematics) , computer science , channel (broadcasting) , computer network , ecology , eigenvalues and eigenvectors , physics , materials science , geometry , quantum mechanics , composite material , biology
This paper describes a new multilevel decomposition method for the separation of convolutive image mixtures. The proposed method uses an adaptive quincunx lifting scheme (AQLS) based on wavelet decomposition to preprocess the input data, followed by a non‐negative matrix factorisation whose role is to unmix the decomposed images. The unmixed images are, thereafter, reconstructed using the inverse of AQLS transform. Experiments carried out on images from various origins showed that the proposed method yields better results than many widely used blind source separation algorithms.