; HGEOM
;---------------------------------------------------------------
;! interpolates image to different gridding and/or geometry
;# Task Catalog Analysis
;-----------------------------------------------------------------------
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;-----------------------------------------------------------------------
HGEOM LLLLLLLLLLLLUUUUUUUUUUUU CCCCCCCCCCCCCCCCCCCCCCCCCCCCC
HGEOM Task to make an image consistent with another image
INNAME Input image (name)
INCLASS (class)
INSEQ 0.0 9999.0 (seq. #)
INDISK 0.0 9.0 (disk unit #)
IN2NAME Second input image (name)
IN2CLASS (class)
IN2SEQ 0.0 9999.0 (seq. #)
IN2DISK 0.0 9.0 (disk unit #)
OUTNAME Output image (name)
OUTCLASS (class)
OUTSEQ -1.0 9999.0 (seq #)
OUTDISK 0.0 9.0 (disk unit #)
BLC 0.0 4096.0 Bottom left corner of image
0=>entire image
TRC 0.0 4096.0 Top right corner of image
0=>entire image
IMSIZE 0.0 4096.0 Output image size in pixels
APARM (1)=terp.ord., (2)=zero flag
(3,4) = new reference pixel
= (0,0) => center in output
----------------------------------------------------------------
HGEOM
Type: Task
Use: HGEOM will compute a non-linear geometric transformation of an
input image using Everett interpolation (bilinear, bicubic, or
biquintic). The transformation is implicitly defined by the
headers of the two pictures. In short, HGEOM "makes this
picture look like that picture". In other words, it transforms
the first picture so that its geometry as described by the
header is consistent with that of the second "template" picture.
HGEOM is therefore useful for data comparison and will probably
grow into a mosaicing task. HGEOM uses the 'scrolling buffer'
concept of how to do geometric transformations. Blanks will
appear in the output wherever the corresponding input pixel is
outside the first image or blanked and the edges and blanked
pixels expand by the width of the interpolation fucntion.
Blanks will also appear if the internal buffer is exhausted but
dynamic memory should ameliorate this. Warning messages are
printed when pixels are blanked.
=====================================================================
We recommend use of OHGEO instead of HGEOM. The latter propogates
blanks and edges onto good areas by the width of the interpolation
function, while OHGEO can even fill in small blanked regions and
extrapolate a little.
=====================================================================
Adverbs:
INNAME.......Input map name. Standard defaults.
INCLASS......Input map class. Standard defaults.
INSEQ........Input map seq no. 0 => highest.
INDISK.......Input map disk: 0 = any.
IN2NAME......Input template map name. Standard defaults.
IN2CLASS.....Input template map class. Standard defaults.
IN2SEQ.......Input template map seq no. 0 => highest.
IN2DISK......Input template map disk: 0 = any.
OUTNAME......Output map name. Standard defaults.
OUTCLASS.....Output map class. Standard defaults.
OUTSEQ.......Output map seq no. 0 => highest unique.
OUTDISK......Output map disk. 0 => highest with room.
BLC..........The bottom left-hand pixel of the input image which
becomes the bottom left corner of the input subimage.
The value (0,0) means (1,1).
TRC..........The top right-hand pixel of the input image which
becomes the top right corner of the subimage. The
value (0,0) means take the top right hand corner of the
image.
IMSIZE.......Output image size in pixels [1=columns, 2=rows]
Default is second input image size. The output size is
independent of the input size. If IMSIZE is all 0 and
APARM(3) and APARM(4) are zero, then the reference
pixels are taken from the 2nd image.
APARM........Transformation parameters:
1 = Order of Everett interpolation formula
[range 1-7: 1=bilinear, 3=bicubic,
5=biquintic, 7=biseptic] 0 => 3.
Higher orders will produce more accurate
interpolations although oddities may appear when
the output cell size is significantly smaller than
the input cell size. See extensive discussion in
EXPLAIN file.
2 = zero-flag. If > 0.0, zeroes are used wherever
blanks would normally be created in the output
image. Else "magic value" blanks are used.
3 = X reference pixel in output pixels;
4 = Y reference pixel in output pixels; If BOTH
APARM(3) and APARM(4) are zero, but IMSIZE is not, then
the numeric center of the input (sub)image is placed at
the numeric center of the output image. If IMSIZE is
all 0 and both APARM(3) and APARM(4) are zero, then the
reference pixels are taken as the reference pixels of
the 2nd image.
----------------------------------------------------------------
Explanation of various details of HGEOM
(DCW, 11 October 1984)
(Modified by W. Cotton Feb, 1993)
On the Relation of Input Image Size to Output Image Size:
The adverb IMSIZE specifies the dimensions of the output image. If it
is set to zero then the default applies and the output image is the
same size as the second input. But if it is nonzero it means EXACTLY
what it says: the output will have dimensions specified by IMSIZE
regardless of what the dimensions of the input image happen to be.
Suppose the input image is 800-square and BLC and TRC are zero (i.e.,
all of the image). If IMSIZE is 100-square then the output is
100-square. On the other hand, if IMSIZE is bigger than the input,
say 1000-square, the result is that the input maps to the output and
regions which are outside the input image will be blank in the output.
Note that the portion of the input which is used is delimited by BLC
and TRC completely independently of IMSIZE.
The pixel position of the "reference pixel" is specified by the
APARM(3) and APARM(4) adverbs. If BOTH are 0 AND IMSIZE is NOT 0,
then the input image center (after application of BLC and TRC) is
mapped to the numeric center of the output image. If APARM(3),
APARM(4), and IMSIZE are all zero, then APARM(3) and APARM(4) are set
to the reference pixels of the second image (and IMSIZE is set to the
size of the second image). The pixel values given in APARM(3) and
APARM(4) are those in the output image. Things like BLC, TRC and
IMSIZE are ignored when one or both are non-zero.
A Discussion of Blanking in HGEOM:
Output pixels are computed as a weighted sum of the input pixels in
the neighborhood of the computed location in the input (the
interpolation weights depend on the fraction of the pixel spacing). If
one of the input pixel values in the sum is blanked, i.e., indefinite
in value, then the whole sum, i.e. the output pixel, is blanked. If
APARM(2) is true, then instead of blank a zero is delivered as the
output pixel value. As a result, if an isolated pixel in the input is
blank it will produce blanks (or zeroes) in a region in the output
image the same size as the interpolating kernel (4x4 pixels for
bicubic, 8x8 for biseptic). This is not nice, but all suggested cures
considered so far have other difficulties.
There is a special case implemented for the edges of the input image.
Here HGEOM "reflects" the pixels near the boundary out into the terra
incognita and uses them for interpolation. This means that pixel 1 is
copied into the pixel 0 position, pixel 2 is copied into pixel -1,
etc. The effect of this algorithm, implemented on all four edges, is
that first derivatives of the interpolant are zero at all boundaries.
NOTE: "Boundary" in this case also means the window defined by BLC and
TRC.
If the output image is larger than the transformation of the input
window then the extra area will be blanked (or zeroed).
Regarding the Everett Interpolation Formula:
This program computes where in the input image each output pixel
appears. This location is generally at some arbitrary fractional pixel
position in x and y, surrounded by four input pixels. The program then
interpolates in the pixel values surrounding the position. For
APARM(1)=1 it uses linear interpolation, first in x (twice) and then
in y. This is called "bilinear" interpolation, and is the default
choice.
If APARM(1)=3 two pixels on each side in x and y are used, sixteen
pixels total. A cubic polynomial is fitted through the four pixels in
each of the four rows in x, and evaluated at the computed x-position.
Then a cubic is put through those four values (in y) and evaluated.
This is "bicubic" interpolation. For APARM(1)=5 we use a 6x6 array,
36 pixels total; this is "biquintic" interpolation. For APARM(1)=7, we
have 8x8, 64 pixels, "biseptic" interpolation.
Regarding the Numerical Accuracy of Interpolation:
The accuracy of interpolation increases with the order of the
polynomial, but of course so does the cost of computation. As the
polynomial order increases the interpolating function approaches ever
more closely to the sinc function, the perfect bandlimited
interpolator, as a limit. Bicubic is good enough for many ordinary
purposes. Biquintic will probably be good enough even for high dynamic
range data like optical CCD imagery and self-calibrated VLA maps. The
point is, if we want very accurate results we want the "noise" of
interpolation errors to be less than the observational noise in the
data at all spatial frequencies of interest.
The interpolation is done by computing a set of interpolating weights
which are a function of the fractional pixel position where the answer
is desired. The answer is then computed by multiplying the weights by
the data. This is a convolution. It follows that an interpolator of a
given order has a corresponding transfer function. I.e., in the
terminology of aperture synthesis, it acts somewhat like a peculiar,
probably "bumpy" taper function in the u-v plane. This function is
always going to be less than unity in the neighborhood of the Nyquist
frequency of the map. The size of this "neighborhood" where the
transfer function is significantly different from unity is smaller for
higher order polynomials. It follows that maps which are well sampled,
say three or four pixels per beam, are easier to interpolate
accurately.
How to Evaluate the Numerical Errors of Interpolation:
DCW is not aware of any proper quantitative treatment of the
interpolation accuracy issues anywhere in the literature of image
processing. This situation is not as bad as it seems because for
critical work there are two simple tests which can be used to estimate
the interpolation error:
Test1: compute a small change, reverse it, then difference input and
output. Use COMB to subtract the second output from the input. The
difference is all interpolation noise. If that noise level (due to TWO
interpolations) is small enough to suit you then you know that the
noise of the first answer is even lower (only one interpolation).
Actually, this test is excessively conservative because the transfer
function of interpolation is SQUARED by the double calculation (NOTE:
any multiple application of interpolation to data will square, cube,
etc., the transfer function of data. Mostly one loses high spatial
frequencies in the data, but errors creep into the lower spatial
frequencies, too.)
Test2: compute a small change with one order of interpolation, compute
again with increased order, then difference the two results. Use COMB
to subtract the the first answer from the second. The difference is an
estimate of the error of bicubic interpolation for your image. An even
more accurate estimate of the error would be obtained by using
APARM(1)=7 (biseptic) for the second computation. Although this test
does not inspire the degree of conviction which Test1 can by
reproducing the input image, it more than makes up for this by giving
an estimate of the interpolation error of a SINGLE operation.
NOTE: in performing tests such as are suggested above it will make
sense to use BLC and TRC to select a small region of the input image
(say, 100-square) which contains interesting structure whose details
need to be preserved. This will save time and make it reasonable to
perform both Test1 and Test2, and to use biseptic interpolation for
Test2. For example, one could perform Test1 using biseptic
interpolation in order to be convinced that it's results are accurate.
Then perform Test2 for bicubic and/or biquintic and compare them to
the biseptic computation.