Deconvolution of maps of Stokes parameters using a PCA regularization
This deconvolution method follows the scheme presented in Ruiz Cobo & Asensio Ramos (2013) The Stokes parameters are projected onto a few spectral eigenvectors and the ensuing maps of coefficients are deconvolved using a standard Lucy-Richardson algorithm. This introduces a stabilization because the PCA filtering reduces the amount of noise.
In order to use it, make sure you read the Stokes parameters and run the deconvolveAll
routine.
stokes = ['I','Q','U','V']
npca = [10,10,10,10]
iter = [50,25,25,25]
psf_file = 'psf.fits' ; This file is not provided in this distribution
for i = 0, 3 do begin
; Read your Stokes parameter file and enter it into the next routine
; For instance, use restore, 'stokes'+stokes+'.idl' if you have the
; Stokes parameters saved on different files
precalculatePCA, data, stokes[i]
computePCAMaps, stokes[i], npca[i]
deconvolutionPCA, stokes[i], psf_file, iter[i], npca=npca[i]
endfor