期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 16, 期 11, 页码 2675-2681出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2007.907073
关键词
feature extraction; morphological component analysis (MCA); sparse representations
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 l(1) norm, but MCA is much faster and gives us the possibility of handling large scale data sets.
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