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Last modified: December 2013

URL: http://cxc.harvard.edu/ciao/ahelp/dmimgadapt.html
AHELP for CIAO 4.9

dmimgadapt

Context: tools

Synopsis

Adaptively smoothes an image based on number of counts

Syntax

dmimgadapt  infile outfile function minrad maxrad numrad radscale
counts [inradfile] [innormfile] [sumfile] [normfile] [radfile]
[clobber] [verbose]

Description

`dmimgadapt' performs (yet another) type of adaptive image smoothing. The output image will tend to preserve small scale features while larger features get smoothed out over a larger area.

The algorithm used is a two-pass method. During the first iteration, the image at any given pixel is convolved w/ one of the convolution kernels. The size of the convolution kernel is increased until the convolution sum is greater-than or equal to the 'counts' parameter value or the 'maxrad' scale is reached. This convolution sum is (optionally) saved in the 'sumfile' output file.

Unlike csmooth, which smoothes the entire image at each scale, `dmimgadapt' smoothes each pixel with only the scales it needs to, to find where the counts threshold is exceeded. For speed reasons we make use of the fact that within an image we are more-likely than not to want to use a similar sized scale to the 'last' pixel smoothed. So instead of starting at the min and looping through to the max, we start at the last scale that was used, if the counts are exceeded we step backwards through the scales until we drop below the threshold. If counts are not met, then we increase scales until we exceed threshold. Since edges are typically smoothed at large scales, this speedup is equally useful when going from row-to-row at the last and then first column of the image.

Since the smoothing scale includes contribution from other pixels we have to keep track of how much each pixel is used in smoothing all the other pixels. This provides a natural flux-preserving normalization to the input data. This value is output in the 'normfile'.

Only after all the scale sizes have been determined is the normalization map complete. So after all the scale sizes are found, the input data are renormalized and the final pass at smoothing the normalized data is performed. The 'radfile' will optionally contain the smoothing scales used. The 'outfile' will contain the final smoothed image.

dmimgadapt will respect the NULL or NaN values in the input image. Also any image subspace (region filter) is respected. Edges are clipped so that the mask is again reduced in size.

Users can also supply their own scale file (eg input the radfile from a previous run). This allows users to do something like smooth two different energy bands with the same scales.

Example 1

dmimgadapt in.fits out.fits func=tophat min=0.1 max=10 num=20 rad=log
counts=30

Smoothed the image in 'in.fits' with a tophat function with radii/scales ranging from 0.1 to 10. There are 20 such scales spaced logarithmically between the min and max. The output image will have had 30 counts per-pixel when the data were convolved.

Example 2

dmimgadapt in.fits out.fits func=gaussian min=1.0 max=30 num=30
rad=linear counts=30 sumfile=sum.fits normfile=norm.fits
radfile=scale.fits

The input image is convolved w/ Gaussian functions with sigma values ranging from 1 to 30 image pixels in size (in 1 pixel increments). The output image will have had 30 counts per pixel when convolved w/ the Gaussian of the determined size.

The diagnostic images sum.fits will contain the convolved sums, norm.fits indicates how much each input pixels was used by all the output pixels, and scale.fits shows the smoothing scales used at each pixel.

Example 3

dmimgadapt in.fits out.fits func=gaussian min=1.0 max=30 num=30
rad=linear counts=30 sumfile=sum.fits normfile=norm.fits
radfile=scale.fits
dmimgadapt in_2.fits out_2.fits func=gaussian inradfile=scale.fits
mode=h

Similar to above, but the 2nd time dmimgadapt is run, instead of supplying the min, max, number, scaling, and min counts, we are using the output scale.fits file from the previous run to smooth a second image with the same scales.

Because we did not supply an innormfile, the flux in the output image will not exactly equal the flux in the input image.

Parameters

name type ftype def min max reqd stacks
infile file input       yes no
outfile file output       yes  
function string   tophat     yes  
minrad real   0.5     yes  
maxrad real   10     yes  
numrad integer   20     yes  
radscale real   log     yes  
counts real   0     yes  
inradfile file         no  
innormfile file         no  
sumfile real output       no  
normfile real output       no  
radfile real output       no  
clobber boolean   no        
verbose integer   0 0 5    

Detailed Parameter Descriptions

Parameter=infile (file required filetype=input stacks=no)

The input image.

The input 2D image.

Parameter=outfile (file required filetype=output)

The output file name

The output smoothed image.

Parameter=function (string required default=tophat)

The convolution filter function to use.

The allowed values for 'function' are "tophat", "boxcar", "gaussian", "cone", "pyramid", "exp", "lor" (Beta/Lorentzian), "walrewop" (aka "powerlaw"), "hemisphere", and "quad" which is the Epanechnikov quadratic.

Note: The kernels are computed such that fractional area covered is by the kernels are included. They are then explicity normalized to have a max value =1. So, while a tophat w/ radii =0.1 and 0.2 will be identical (a single pixel=1), a tophat w/ radii = 1.1 and 1.5 will be different.

Parameter=minrad (real required default=0.5)

Minimum smoothing scale

The smallest radius or scale that the image will be smoothed at. All scales are in image pixels.

Parameter=maxrad (real required default=10)

Maximum smoothing scale

The largest radius or scale that the image will be smoothed at. Larger smoothing scales take longer, so users may want to do one run with a medium sized scale and see which pixels did not meet the min. number of counts criteria by examining the 'sumfile' and then possibly increasing the maxrad if desired. All scales are in image pixels.

Parameter=numrad (integer required default=20)

The number of radii/scales to use

The more scales that are used, the more 'natural' the smoothing will appear; however, as more scales are added the run-time will increase.

Parameter=radscale (real required default=log)

Controls the spacing between the radii

The radii can either be created on a linear scale or a logarithmic scale. The former is useful when there are features of uniformly varying size in the image. The later will work harder on smaller features in the image and smooth larger features at more coarse resolutions.

Parameter=counts (real required default=0)

The minimum number of counts 'under' the kernel.

The minimum integral of the kernel*image. If the minimum number of counts is not met, the kernel size is increased up to the max.

Parameter=inradfile (file not required default=)

Image with pre-determined smoothing scales

Users can supply their own image with pre-determined scales. This can be the output stored in the "radfile" file from a previous run or any other image that has the same dimensions.

When inradfile is used, the following parameters are not used: minrad, maxrad, numrad, radscale, and counts.

Note that it is possible to use a different function (tophat, Gaussian, etc) than was used to create the inradfile. (Since image does not have to have been created by dmimgadapt.)

Parameter=innormfile (file not required default=)

Image with pre-determined normalizations

To preserve flux the normalizaitons to the image must be supplied as a second input file. HOWEVER, these normalizations will impose artifacts of the originally smoothed image onto the current input. For example, if one tries to smooth an exposure map with the same scales (inradfile) and normalization (innormfile) as a counts image, the output image will have depressions where there are point sources in the counts image. Flux will be preserved between the infile and outfile.

If the innormfile is not supplied then a value of 1 is used for all pixels (can be seen if output normfile is requested). This will not preserve flux between the input and output images; but the output image will not have structure in the original input image imposed on it.

When should innormfile be used? If you want to smooth the same data with the same scales then you probably want to supply the innormfile. If you are smoothing an image with the same units (eg counts) then you may want to provide the innormfile. When smoothing an image with different units (eg exposure) then leaving innormfile="" or "none" is best. If inradfile is not supplied then innormfile is ignored.

Parameter=sumfile (real not required filetype=output default=)

Image with sum determined in the first pass.

will be greater-than or equal to 'counts' (unless maxscale was too small).

Parameter=normfile (real not required filetype=output default=)

Image with the data normalization

Each input image pixel contributes to several output pixels. This image shows how much each input pixel was used when making the output image.

Parameter=radfile (real not required filetype=output default=)

The smoothing scales

This image shows the smoothing scales/radii used at each pixel location.

Parameter=clobber (boolean default=no)

Remove output if it exists?

Used to specify whether or not to clobber existing file that has the same name as the specified output file

Parameter=verbose (integer default=0 min=0 max=5)

The tool chatter level

Verbose can be from 0 to 5, generating different amounts of debugging output.

Bugs

There are no known bugs for this tool.

See Also

dm
dmfiltering
tools
dmappend, dmcontour, dmellipse, dmfilth, dmimg2jpg, dmimgblob, dmimgcalc, dmimgdist, dmimgfilt, dmimghist, dmimghull, dmimglasso, dmimgpick, dmimgpm, dmimgproject, dmimgreproject, dmimgthresh, dmmaskbin, dmmaskfill, dmnautilus, dmregrid, dmregrid2, dmstat, evalpos, imgmoment, mean_energy_map, pileup_map

Last modified: December 2013
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