cea-01887796
https://cea.hal.science/cea-01887796
doi:10.1109/ICIP.2014.7026215
[CEA] CEA - Commissariat à l'énergie atomique
[INSMI] CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions
[DSM-IRFU] IRFU
[DRT] Direction de la recherche technologique
[TDS-MACS] Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes
[CEA-DRF] Direction de Recherche Fondamentale
[LIST] Laboratoire d'Intégration des Systèmes et des Technologies
[DM2I] Département Métrologie Instrumentation & Information
[DIN] Département d'Instrumentation Numérique
Sparse blind source separation for partially correlated sources
Bobin, J.
Starck, J.
Rapin, J.
Larue, A.
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
[MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA]
[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]
[STAT.AP] Statistics [stat]/Applications [stat.AP]
COMM
Sparsity
wavelets
blind source separation
morphological diversity
Correlation
Standards
Algorithm design and analysis
RNA
Sparse matrices
statistical analysis
partially correlated sources
sparsity-enforcing BSS method
adaptive morphological component analysis
AMCA
adaptive weighting scheme
Blind source separation (BSS) is a very popular technique to analyze data which can be modeled as linear mixtures of elementary sources. Standard approaches generally make the assumption that such sources are statistically independent or at least uncorrelated. However, this is barely the case for real-world sources which are very often partially correlated. We present a new sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA) designed to retrieve sparse and partially correlated sources based on an adaptive weighting scheme. Numerical experiments have been carried out which show that the proposed method is robust to the partial correlation of the sources while standard BSS techniques fail. The performances of the proposed algorithm are further illustrated with simulations in the context of astrophysics.
2014-10-27
en
IEEE
2014 IEEE International Conference on Image Processing (ICIP)
Paris, France