Brighter Fatter Kernel calibration definition.
| lsst.ip.isr.brighterFatterKernel.brighterFatterCorrection |
( |
| exposure, |
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| kernel, |
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| maxIter, |
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| threshold, |
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| applyGain, |
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| gains = None ) |
Apply brighter fatter correction in place for the image.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure to have brighter-fatter correction applied. Modified
by this method.
kernel : `numpy.ndarray`
Brighter-fatter kernel to apply.
maxIter : scalar
Number of correction iterations to run.
threshold : scalar
Convergence threshold in terms of the sum of absolute
deviations between an iteration and the previous one.
applyGain : `Bool`
If True, then the exposure values are scaled by the gain prior
to correction.
gains : `dict` [`str`, `float`]
A dictionary, keyed by amplifier name, of the gains to use.
If gains is None, the nominal gains in the amplifier object are used.
Returns
-------
diff : `float`
Final difference between iterations achieved in correction.
iteration : `int`
Number of iterations used to calculate correction.
Notes
-----
This correction takes a kernel that has been derived from flat
field images to redistribute the charge. The gradient of the
kernel is the deflection field due to the accumulated charge.
Given the original image I(x) and the kernel K(x) we can compute
the corrected image Ic(x) using the following equation:
Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
To evaluate the derivative term we expand it as follows:
0.5 * ( d/dx(I(x))*d/dx(int(dy*K(x-y)*I(y)))
+ I(x)*d^2/dx^2(int(dy* K(x-y)*I(y))) )
Because we use the measured counts instead of the incident counts
we apply the correction iteratively to reconstruct the original
counts and the correction. We stop iterating when the summed
difference between the current corrected image and the one from
the previous iteration is below the threshold. We do not require
convergence because the number of iterations is too large a
computational cost. How we define the threshold still needs to be
evaluated, the current default was shown to work reasonably well
on a small set of images. For more information on the method see
DocuShare Document-19407.
The edges as defined by the kernel are not corrected because they
have spurious values due to the convolution.
Definition at line 589 of file brighterFatterKernel.py.
| lsst.ip.isr.brighterFatterKernel.fluxConservingBrighterFatterCorrection |
( |
| exposure, |
|
|
| kernel, |
|
|
| maxIter, |
|
|
| threshold, |
|
|
| applyGain, |
|
|
| gains = None, |
|
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| correctionMode = True ) |
Apply brighter fatter correction in place for the image.
This version presents a modified version of the algorithm
found in ``lsst.ip.isr.isrFunctions.brighterFatterCorrection``
which conserves the image flux, resulting in improved
correction of the cores of stars. The convolution has also been
modified to mitigate edge effects.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure to have brighter-fatter correction applied. Modified
by this method.
kernel : `np.ndarray`
Brighter-fatter kernel to apply.
maxIter : scalar
Number of correction iterations to run.
threshold : scalar
Convergence threshold in terms of the sum of absolute
deviations between an iteration and the previous one.
applyGain : `Bool`
If True, then the exposure values are scaled by the gain prior
to correction.
gains : `dict` [`str`, `float`]
A dictionary, keyed by amplifier name, of the gains to use.
If gains is None, the nominal gains in the amplifier object are used.
correctionMode : `Bool`
If True (default) the function applies correction for BFE. If False,
the code can instead be used to generate a simulation of BFE (sign
change in the direction of the effect)
Returns
-------
diff : `float`
Final difference between iterations achieved in correction.
iteration : `int`
Number of iterations used to calculate correction.
Notes
-----
Modified version of ``lsst.ip.isr.isrFunctions.brighterFatterCorrection``.
This correction takes a kernel that has been derived from flat
field images to redistribute the charge. The gradient of the
kernel is the deflection field due to the accumulated charge.
Given the original image I(x) and the kernel K(x) we can compute
the corrected image Ic(x) using the following equation:
Ic(x) = I(x) + 0.5*d/dx(I(x)*d/dx(int( dy*K(x-y)*I(y))))
Improved algorithm at this step applies the divergence theorem to
obtain a pixelised correction.
Because we use the measured counts instead of the incident counts
we apply the correction iteratively to reconstruct the original
counts and the correction. We stop iterating when the summed
difference between the current corrected image and the one from
the previous iteration is below the threshold. We do not require
convergence because the number of iterations is too large a
computational cost. How we define the threshold still needs to be
evaluated, the current default was shown to work reasonably well
on a small set of images.
Edges are handled in the convolution by padding. This is still not
a physical model for the edge, but avoids discontinuity in the correction.
Author of modified version: Lance.Miller@physics.ox.ac.uk
(see DM-38555).
Definition at line 799 of file brighterFatterKernel.py.
1.13.2