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Manpage of blindDeconvolution
blindDeconvolution
Section: User Commands (1)
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NAME
blindDeconvolution  refine a subvolume using blind deconvolution
SYNOPSIS
blindDeconvolution volName
blindDeconvolution volName psfSize
blindDeconvolution volName psfSize nIters
blindDeconvolution volName psfSize nIters saveFreq
DESCRIPTION
Attempt to refine a subvolume average using the blind
deconvolution algorithm of Hirsch et al (J. Computational Biology
3:335346, 2011) with sparseness priors. The observed volume
is assumed to result from blurring of the true volume by
convolution with an unknown kernel of specified maximum size, plus added
noise. I.e. we assume y = f*x, where the input volume is a potentially
noisy version of y.
Estimates of f, x, and y will be refined iteratively and the final
versions saved to <volName>_<psfSize>_<iterNum> with suffixes
of .psf, .x, and .y, respectively. Often the "sharpened" *.x volume will
the desired output. The *.y volume, while not sharpened, can also be
useful as a denoised version of the input.
Cross sections of both the kernel, y, and x are displayed
during refinement, along with plots of the log of the posterior
probability density, and the residual sum of squared errors.
Both the kernel and true volume are required to be nonnegative. If the input
volume contains any negative values, an offset will be added to
eliminate them before processing. Blind deconvolution is an extremely
illconditioned process. Varying kernel size and the number of iterations
will typically lead to different results. Until external confirmation is
obtained, the most that one can say about a given result is that it is
consistent with the observed data.
 volName

is the path to the MRC file containing the input subvolume.
The volume should be padded to be constant within at least
psfSizevoxels of any edge.
 psfSize

is a single, odd number giving the width of the cubical kernel in
voxels. Initially, all voxels in the kernel will be set to the same
value. If ommitted, a default of ~1/16th of the input volume size will
be used. Kernel size can significantly alter the results; trying kernel
sizes of 5, 7, and 9 voxels is often a good starting point.
 nIters

is the maximum number of refinement iterations to execute. Output files
will be written after completing the specified number of iterations
(Default = 100).
 saveFreq

is an optional integer specifying that output files should also be
written after every <saveFreq> iterations (Default = 10).
AUTHOR
John Heumann
SEE ALSO
PEET(1)
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