Morphological component analysis: an adaptive thresholding strategy.
In a recent paper, a method called morphological component analysis (MCA) has been proposed to separate the texture from the natural part in images. MCA relies on an iterative thresholding algorithm, using a threshold which decreases linearly towards zero along the iterations. This paper shows how the MCA convergence can be drastically improved using the mutual incoherence of the dictionaries associated to the different components. This modified MCA algorithm is then compared to basis pursuit, and experiments show that MCA and BP solutions are similar in terms of sparsity, as measured by the l1 norm, but MCA is much faster and gives us the possibility of handling large scale data sets.
Bobin
Jérôme
J
DAPNIA/SEDI-SAP, Service d'Astrophysique, CEA/Saclay, 91191 Gif sur Yvette, France. jerome.bobin@cea.fr
Starck
Jean-Luc
JL
Fadili
Jalal M
JM
Moudden
Yassir
Y
Donoho
David L
DL
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity