Morphological component analysis: An adaptive thresholding strategy (2007)
| Venue: | IEEE Transactions on Image Processing |
| Citations: | 13 - 5 self |
BibTeX
@ARTICLE{Bobin07morphologicalcomponent,
author = {Jérôme Bobin and Jean-luc Starck and Jalal M. Fadili and Yassir Moudden and David L. Donoho},
title = {Morphological component analysis: An adaptive thresholding strategy},
journal = {IEEE Transactions on Image Processing},
year = {2007},
volume = {16},
pages = {2675--2681}
}
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Abstract
Abstract—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 I norm, but MCA is much faster and gives us the possibility of handling large scale data sets. Index Terms—Feature extraction, morphological component analysis (MCA), sparse representations. I.
