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Miriad offers tasks to convolve images by Gaussians (convol and smooth) and also to convolve images by other images (convol). It can also bin up (or block) an image (imbin).
Note that FFT based algorithms cannot correctly deal with blanked pixels. If your image has a blanked pixels, convol pretends that they are zero when it does the convolution. However, the corresponding pixels are blanked in the output image.
CONVOL | |
map=1331-09.icln | Input image |
beam | Unset |
fwhm=10,20 | FWHM of Gaussian |
pa=45 | Position angle of Gaussian |
out=1331-09.icln2 | Convolved image |
Task generally convol
does its best to scale the output pixels so
that the output image units are Jy/beam. If you are convolving an image
which is already in Jy/beam by Gaussian, then convol
needs the
beam parameters (bmaj, bmin, bpa) to be in the dataset header to
correctly determine the scale factor, and the output effective beam
parameters. It will give you messages about what it thinks its doing as
far as scaling factors go. If you know better than convol, you
can give your own scale factor, via the scale
keyword.
Often you know the output resolution that you want, rather than the resolution
of the Gaussian that you want to convolve with. In this case using
options=final
causes convol
to treat the parameters given by the fwhm
and pa
keywords as the desired final resolution, and to work backwards
to determine the Gaussian that it needs to convolve with to achieve this
result. Again, convol
needs to know the beam parameters (bmaj, bmin, bpa),
and does its best to maintain the intensity units in Jy/beam.
As with convol, the scale of the output image is adjusted so that
it is in units of Jy/beam - and you can override this by setting scale
yourself.
SMOOTH | |
in=1331-09.icln | Input image |
out=1331-09.icln2 | Convolved image |
fwhm=10,20 | FWHM of Gaussian |
pa=45 | Position angle of Gaussian |
options | Unset |
scale | Unset for auto scaling |
CONVOL | |
map=1331-09.icln | Input image |
beam=1331-09.icln2 | Convolving image |
region | Unset for full image |
out=1331-09.convol | Output image |
sigma | Unset |
In the first example, we bin up the first three dimensions of an image by a factor of 2 along the x axis, a factor of 4 along the y axis and a factor of 3 along the z axis.
IMBIN | |
in=gc.icln | Input image |
region | Unset for full image |
bin=2,2,4,4,3,3 | Bin and increments for each axis |
out=gc.icln-rebin | Output image |
In the second example, we pick out every 4th pixel along the z axis.
IMBIN | |
in=gc.xyv | Input image |
region=quarter | Only write out inner quarter |
bin=1,1,1,1,1,4 | Pick out every 4th pixel along third axis |
out=gc-2.xyv | Output image |
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