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I have a matrix of N rows of time-series data. There is a specific noise contaminating measurement of the data that I have some information about.

The noise in the data can be modeled as a poisson distribution that blurs signal from a given column in the matrix to adjacent columns. For example, if the original data should be a single peak surrounded by no signal:

0    0    0    1    0    0    0

The measured signal distributed slightly asymmetrically resulting in something like this:

0.001    0.005    0.1    0.5    0.2    0.001    0

If I have a good model of how the noise is distributing the data between the columns, how can I use this information to deconvolve the matrix into an approximation of the original signal?

JG_Maine
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  • This is more a theory question so I would recommend that you ask it on [dsp.se] rather, and one you understand the theory (i.e. how to design your deconvolution kernel or something like that), come back and ask here if you get stuck programming it – Dan Jun 21 '16 at 11:50
  • Okay, will do that. – JG_Maine Jun 21 '16 at 12:04
  • If anyone finds this message, and wants an answer specific to biological data (which is what I was interested in), try https://cibersort.stanford.edu/ – JG_Maine Mar 29 '17 at 13:57

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