Name
filt - apply a digital filter in spatial domain
Synopsis
filt [parameters] <in> [out]
Description
filt is a cube filter in image domain. Choose the kind of
filter you want to apply, the resulting cube will be a
filtered (plane by plane) version of the input cube.
Parameters
-f name or --filter name
Choose the filter to apply. Supported filters are:
user-linear, dx, dy, d2x, d2y, contour1, contour2,
contour3, contrast1, min, max, median, max-min,
user-morpho. Find their descriptions below.
-p `f1 ... f9'or --val `f1 ... f9'
To be used with filters `user-linear' or `user-mor-
pho', to provide 9 values to define the filter. All
9 values are given enclosed in single quotes, sepa-
rated by one or more blank characters.
Algorithm
In image processing, there are numerous ways to filter an
image. This collection of filters all remain in the spa-
tial domain. The basic idea is to convolve the image with
a 3x3 or 5x5 kernel. For bigger kernels, it is often less
time-consuming to go to Fourier space with an FFT.
If we describe a 3x3 neighborhood as :
pix1 pix2 pix3
pix4 pix5 pix6
pix7 pix8 pix9
The new pixel will be a linear combination of pix1 to
pix9. The combination is then normalized by dividing by
the sum of all coeficients, except for NULL sums where no
normalization is applied.
new = a1.pix1 + a2.pix2 + ... + a9.pix9
('.' denoting multiplication).
An example is a low-pass filter defined by the set of 9
coefficients :
1 1 1
1 1 1
1 1 1
The derivative in x axis can be computed by applying :
-1 0 1
-1 0 1
-1 0 1
And so on. See image processing books for more details
about digital filtering in spatial domain ! If you choose
a user-defined filter, you get prompted for 9 values, that
are the coefficients described above.
For morphological filters, the idea remains the same,
except that pix1 to pix9 are sorted before combination,
that is :
new = a1.min(pix1, .., pix9) + ... + a9.max(pix1, ...,
pix9)
The well-known median filter is then simply described by
the coefficients :
0 0 0 0 1 0 0 0 0
That is, each pixel is replaced by the median value of its
neighborhood. Due to this necessary sorting on every
pixel, morphological filters are usually very computer-
intensive.
Implemented Filters
user-linear
Enter 9 values for a 3x3 linear filter, through the
-p or --val option.
mean3 All coefficients equal to 1
dx X derivative. Filter coefficients are:
-1 0 1
-1 0 1
-1 0 1
dy Y derivative. Filter coefficients are:
-1 -1 -1
0 0 0
1 1 1
d2x X second derivative. Filter coefficients are:
1 -2 1
1 -2 1
1 -2 1
d2y Y second derivative. Filter coefficients are:
1 1 1
-2 -2 -2
1 1 1
contour1
Contour detector. Filter coefficients are:
1 0 -1
0 0 0
-1 0 1
contour2
Another contour detector. Filter coefficients are:
-1 0 1
2 0 -2
-1 0 1
contour3
Yet another contour detector. Filter coefficients
are:
-1 2 -1
0 0 0
1 -2 1
contrast1
Contrast enhancement. Filter coefficients are:
1 1 1
1 4 1
1 1 1
mean5 Low-pass (smearing) filter. All coefficients are
set to 1 on a 5x5 neighborhood.
min Morphologival minimum. Filter coefficients are
(from min to max) :
1 0 0 0 0 0 0 0 0
max Morphological maximum. Filter coefficients are
(from min to max) :
0 0 0 0 0 0 0 0 1
median Morphological median. Filter coefficients are (from
min to max) :
0 0 0 0 1 0 0 0 0
max-min
Morphological max-min. Filter coefficients are
(from min to max) :
-1 0 0 0 0 0 0 0 1
user-morpho
Provide 9 values for a 3x3 morphological filter,
through the -p or --val option.
flat This filter applies an NxN convolution with a
matrix filled with ones only. This is a low-pass
filter. The size of the convolution kernel to use
can be set through the -p or --val option, provid-
ing a single value which defines the kernel half-
size. Example: calling a flat filter with the
option -p 2 will filter the image with a 5x5 kernel
containing only ones everywhere. The computation
time increases tremendously with the kernel size,
it is not recommended to use too large kernels.
Files
Input files shall all comply with FITS format.
The original FITS header of the input FITS file is con-
served along, except for the following keywords: NAXIS,
NAXISn, BITPIX, BSCALE, BZERO, which are related to the
newly created file.
HISTORY keywords are appended to the FITS header to indi-
cate the eclipse process modifications.
The output file naming scheme is: *.fits becomes *.fil-
ter.fits.
Examples
To apply an x derivative to a file named `in.fits', do:
filt --filter dx in.fits
the created output file is named `in.dx.fits'.
To apply a median filter to a file named `m51.fits', do:
filt --filter median m51.fits
the created output file is named `m51.median.fits'/
To apply a user-defined morphological filter to
`sofi.fits':
filt --filter user-morpho --val `1 0 0 0 1 0 0 0 1'
sofi.fits
To apply a flat low-pass filter with a 21x21 kernel to a
file named `m51.fits', do:
filt --filter flat --val 10 m51.fits
See Also
fft, ccube