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sclean stsdas.analysis.restore


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SEE_ALSO

NAME

sclean -- Sigma-CLEAN deconvolution of 2-d images.

USAGE

sclean input psf output residual

DESCRIPTION

Standard CLEAN works by iteratively building the so-called "CLEAN map" from the input image and PSF. The algorithm proceeds as follows:

(1)
Copy the input image to a new image, called "residual map". Clear (zero) the CLEAN map.
(2)
Locate the largest (in absolute value) peak in the residual map.
(3)
At its corresponding position in the CLEAN map, add a Dirac-delta-like contribution ("CLEAN component"). Its amplitude is a function of the residual map peak value and the so-called "CLEAN loop gain" (a value less than 1.).
(4)
Subtract a scaled version of the PSF from the residual map, at the peak position.
(5)
Iterate to step (2), or stop if some convergency criterion is met.

The basic difference between standard CLEAN and Keel's sigma-CLEAN is in the way it locates features to be subtracted from the residual map. Standard CLEAN looks for the largest peak in the residual map. Sigma-CLEAN builds, at each iteration, an auxiliary "signal-to-noise map", and looks there for the largest peak instead. In this way, information from the noise in the input image is constantly used to control the iterations, presumably giving better stability properties to the method. Refer to the reference for details.

This task implements some additional features not contained in Keel's code (as published in the below-cited paper):

o
Optionally, the CLEANed image can be convolved with a so-called "restored beam", in order to dampen very high, non-realistic spatial frequencies present in the CLEANed image. This convolution can be implemented either by a direct operation or in Fourier space. The direct operation uses a Gaussian kernel, and may be adequate when its FWHM is small. For larger values of FWHM, Fourier convolution may be faster. However, it may result in some faint ringing appearing around very bright point sources. The FFT algorithm used by the task is faster when the axis sizes are composite numbers (non-primes), faster yet when rich in factors of 2, and even faster when exact powers of 2. Alternatively, the restored beam convolution may be done outside the task, for instance using task gauss in package images.
o
Optionally, the residual image remaining after iterations have ended may be added to the CLEANed convolved image. This is useful to preserve noise properties of the input image into the CLEANed image.
o
The convergency criterion adopted in the original code showed to be too strong, usually stopping the algorithm at the very first iterations. The present implementation adopts a modified stopping mechanism: iterations are terminated when the S/N peak value beign processed drops below a user-supplied parameter (task parameter `crit'), meaning that CLEAN components started to dig into noise. To lessen the sensitivity to oscillations, the actual comparison includes the 5 last iterations. The algorithm is stopped only when all of them have peak S/N values below the stopping parameter .

The current implementation also includes a much faster (albeit more complex) peak finding algorithm.

The PSF image must be read from a separate file from the input image. The PSF does not need to be accurately centered in the array. The pixel with maximum intensity will be taken as the PSF center. However, to speed up PSF addressing in the code, it is assumed that the precise PSF center coincides with the center of a pixel. Also, it is assumed that the PSF image goes to zero at the borders of the PSF array.

The task can process an image template or list in input. In this case, output is either a matching list of images or a directory. Both psf and residual are, however, always single images. If a multiple image list or template is used, the residual image will correspond to the first image only; residual image output is disabled for the remaining input images.

HISTORY records containing information on algorithm parameters are appended to the output image header.

Typical CPU time is 0.2 seconds per iteration (Sparc 2) for a 512 X 512 image and 100 X 100 PSF.

PARAMETERS

input [file name template]
Input 2-d image(s) section(s) to be deconvolved. This can also be a file list.
psf [file name]
Input PSF 2-d image section.
output [file name list]
Output deconvolved image(s). Always type real, regardless of input image type. This parameter can also be a directory name.
residual = "" [file name]
Output residual image. If left as a null string (""), no residual image is produced.
(noise = 0.) [real, min=0.]
Effective readout noise in electrons.
(adu = 1.) [real, min=0.]
A/D conversion constant, electrons/DN.
(cgain = 0.02) [real, min=0., max=1.]
CLEAN loop gain.
(crit = 2.7) [real, min=1.]
Stopping criterion, in S/N units.
(maxloop = 1000) [int, min=1]
Maximum allowed number of iterations, independently from the stopping criterion given by parameter `crit'.
(fwhm = 2.0) [real, min=0.]
Restored beam FWHM. If set to zero, no convolution takes place.
(fft = no) [boolean]
Perform convolution in Fourier space?
(addres = yes) [boolean]
Add residual map to CLEAN map?
(verbose = 0) [integer, min=0, max=1]
Verbosity level.
  0 - No output
  1 - Print the file name and execution time
  2 - Print iteration number, peak position, peak value, clean
      component, and peak S/N

EXAMPLES

BUGS

REFERENCES

This task implements the sigma-CLEAN deconvolution algorithm described in:

Keel, W., 1991, PASP, 103, p. 723.
This task was written by I.Busko

SEE ALSO


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