The Morphological Component Analysis (MCA) is a a new method which allows us to separate features contained in an image when these features present different morphological aspects. We show that MCA can be very useful for inpainting applications.
We extend MCA to a multichannel MCA for analyzing multispectral data which leads to a new approach for blind source separation, based on the morphological diversity concept instead of the statistical independence of the source. We apply MCA on two different data set: the WMAP cosmic microwave data and the PLANCK simulations.
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