Viewing a response to: @alexs1320/math-from-finland-yes-it-s-fast-fastica
Thank you for the shoutout @alexs1320 ! I had to go away and think about how this would be applied to image processi ng (for astronomical images of course) and there could be some interesting applications to explore. From what I read this is a technique of estimating the original contributions of multiple signals embedded in a single signal. I will definitely keep this and your links bookmarked!
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| author | terrylovejoy |
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(Probably) the best algorithm would be the SOBI-RO from the package ICALAB, by Andrzej Cichocki. That obscure method works with everything I've ever put as the input. It's especially good for sparse signals. In your case, the workflow would be: each image --> image to vector (in Matlab) --> all the vectors packed into the input matrix --> SOBI-RO --> back to images (vector to matrix) --> and... see what is the signal and what is the noise :) --> discard the noise From *n* images, *n* components could be extracted (both in ICA and SOBI), in contrast to PCA/FA where you need *2n+1* starting signals for only *n* components,
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| author | alexs1320 |
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