/
OSSPS_QuadraticPrior.par
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OSSPS_QuadraticPrior.par
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OSSPSParameters :=
; example file for OSSPS using a quadratic prior
; this is a minimal file. check other sample files and doc for more options
objective function type:= PoissonLogLikelihoodWithLinearModelForMeanAndProjData
PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:=
input file := test.hs
; if disabled, defaults to maximum segment number in the file
maximum absolute segment number to process := 4
; see User's Guide to see when you need this
zero end planes of segment 0:= 0
projector pair type := Matrix
Projector Pair Using Matrix Parameters :=
Matrix type := Ray Tracing
Ray tracing matrix parameters :=
End Ray tracing matrix parameters :=
End Projector Pair Using Matrix Parameters :=
; put normalisation (and attenuation) here
; Bin Normalisation type := From ProjData
; Bin Normalisation From ProjData :=
; normalisation projdata filename:= norm.hs
; End Bin Normalisation From ProjData:=
; specify additive projection data to handle randoms or so
; see User's Guide for more info
additive sinogram := 0
; if the next parameter is disabled,
; the sensitivity will be computed
;sensitivity filename:= sens.hv
; here comes the prior stuff
prior type := quadratic
Quadratic Prior Parameters:=
penalisation factor := 1
; next can be used to set weights explicitly. Needs to be a 3D array (of floats).
' value of only_2D is ignored
; following example uses 2D 'nearest neighbour' penalty
weights:={{{0,1,0},{1,0,1},{0,1,0}}}
; use next parameter to specify an image with penalisation factors (a la Fessler)
; see class documentation for more info
; kappa filename:=
; use next parameter to get gradient images at every subiteration
; see class documentation
; gradient filename prefix:=
END Quadratic Prior Parameters:=
end PoissonLogLikelihoodWithLinearModelForMeanAndProjData Parameters:=
initial estimate:= some_image
; you could enable this when you read an initial estimate with negative data,
; although OSSPS does not need it
; enforce initial positivity condition:=0
output filename prefix := test_QP
number of subsets:= 12
number of subiterations:= 24
save estimates at subiteration intervals:= 12
; here start OSSPS specific values
; values to use for the 'precomputed denominator'
; if you do not specify the following keyword, the 'precomputed denominator'
; will be computed automatically (and saved)
; use the following if you have it already (e.g. from previous run)
; note: setting the value to 1 will use an images full of ones (which is not a good idea!)
; precomputed denominator := my_precomputed_denominator.hv
; specify relaxation scheme
; lambda = relaxation_parameter/ (1+relaxation_gamma*(subiteration_num/num_subsets)
relaxation parameter := 1
relaxation gamma:=.1
; you can give an upper bound on the image values.
; lower bound is always zero.
;upper bound:= 1
END :=