This paper describes a new method for blind source separation, adapted to the case of sources
having different morphologies. We show that such morphological diversity leads to a new and
very efficient separation method, even in the presence of noise. The algorithm, coined MMCA
(Multichannel Morphological Component Analysis), is an extension of the
Morphological Component Analysis method (MCA). The latter takes advantage of the
sparse representation of structured data in large overcomplete dictionaries to separate
features in the data based on their morphology. MCA has been shown to be an efficient
technique in such problems as separating an image into texture and piecewise smooth
parts or for inpainting applications. The proposed extension, MMCA, extends the above
for multichannel data, achieving a better source separation in those circumstances.
Furthermore, the new algorithm can efficiently achieve good separation in a noisy
context where standard ICA methods fail. The efficiency of the proposed scheme
is confirmed in numerical experiments. |
