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mcaMorphological Component Analysis of a 2D images (a matrix) using highly redundant dictionaries. MCA is very useful for separating sources based on their morphological diversity. Synopsismca itermax cartoon gamma expdecrease stop [dict_in|-] [im_in|-] [im_out|-]DescriptionThe MCA solves the ollowing optimization problem using a modified version of the BCR algorithm:(part_a,part_b) = argmin0.5 ||img - Sum_i part_i||_2^2 + lambda * Sum_i || \Phi^T_i part_i ||_p + gamma * TV(cartoon part) p = 1 (l_1 norm: Soft thesholding as a solution). p = 0 (l_0 norm: difficult but approximated with a Hard thresholding). Each component part_i is supposed to be sparsely described in its corresponding dictionary \Phi. ParametersitermaxNb of relaxation iterations
cartoon
Index of the part considered as
the cartoon. This index must match one of the transforms (parts) in the
dictionary. TV constraint is not allowed on some transforms, e.g.
corresponding to warped oscillatory patterns.
gamma
TV regularization parameter
(usually applied to the cartoon smooth component, e.g. sparsely
represented by a wavelet or curvelet dictionary.
expdecrease
Exponential/Linear decrease of
the regularization parameter.
stop
Stop criterion, the algorithm
stops when lambda <= stop*sigma (typically k=3), sigma is the noise
WGN std.
Inputs
Outputs
ResultRetruns SUCCESS or FAILURE.See also
dictionary, eminpaint
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