FFCLEAN

Removes defects from a substantially flat one-, or two-, or three-dimensional NDF

Description:

This application cleans a one- or two-dimensional NDF  by removing defects smaller than a specified size. In addition, three-dimensional NDFs can be cleaned by processing each row or plane within it using the one- or two-dimensional algorithm (see Parameter AXES).

The defects are flagged  with the bad value. The defects are found by looking for pixels that deviate from the spectrum or image’s smoothed version by more than an arbitrary number of standard deviations from the local mean, and that lie within a specified range of values. Therefore, the data array must be substantially flat. The data variances provide the local noise estimate for the threshold, but if these are not available a variance for the whole of the data array is derived from the mean squared deviations of the original and smoothed versions. The smoothed version of the data array is obtained by block averaging over a rectangular box. An iterative process progressively removes the outliers from the data array.

Usage:

ffclean in out clip box [thresh] [wlim]

Parameters:

AXES( 2 ) = _INTEGER (Read)
The indices of up to two axes that span the rows or planes that are to be cleaned. If only one value is supplied, then the NDF is processed as a set of one-dimensional spectra parallel to the specified pixel axis. If two values are supplied, then the NDF is processed as a set of two-dimensional images spanned by the given axes. Thus, a two-dimensional NDF can be processed either as a single two-dimensional image or as a set of one-dimensional spectra. Likewise, a three-dimensional NDF can be processed either as a set of two-dimensional images or a set of one-dimensional spectra. By default, a two-dimensional NDF is processed as a single two-dimensional image, and a three-dimensional NDF is processed as a set of one-dimensional spectra (the spectral axis is chosen by examining the WCS component–-pixel-axis 1 is used if the current WCS frame does not contain a spectral axis). []
BOX( 2 ) = _INTEGER (Read)
The x and y sizes (in pixels) of the rectangular box to be applied to smooth the image. If only a single value is given, then it will be duplicated so that a square filter is used except where the image is one-dimensional for which the box size along the insignificant dimension is set to 1. The values given will be rounded up to positive odd integers if necessary.
CLIP( ) = _REAL (Read)
The number of standard deviations for the rejection threshold of each iteration. Pixels that deviate from their counterpart in the smoothed image by more than CLIP times the noise are made bad. The number of values given specifies the number of iterations. Values should lie in the range 0.5–100. Up to one hundred values may be given. [3.0, 3.0, 3.0]
GENVAR = _LOGICAL (Read)
If TRUE, the noise level implied by the deviations from the local mean over the supplied box size are stored in the output VARIANCE component. This noise level has a constant value over the whole NDF (or over each section of the NDF if the NDF is being processed in sections–-see Parameter AXES). This constant noise level is also displayed on the screen if the current message-reporting level is at least NORMAL. If GENVAR is FALSE, then the output variances will be copied from the input variances (if the input NDF has no variances, then the output NDF will not have any variances either). ). [FALSE]
IN = NDF (Read)
The one- or two-dimensional NDF containing the input image to be cleaned.
OUT = NDF (Write)
The NDF to contain the cleaned image.
THRESH( 2 ) = _DOUBLE (Read)
The range between which data values must lie if cleaning is to occur. Thus it is possible to clean the background without removing the cores of images by a judicious choice of these thresholds. If null, !, is given, then there is no limit on the data range. [!]
TITLE = LITERAL (Read)
The title of the output NDF. A null (!) value means using the title of the input NDF. [!]
WLIM = _REAL (Read)
If the input image contains bad pixels, then this parameter may be used to determine the number of good pixels which must be present within the smoothing box before a valid output pixel is generated. It can be used, for example, to prevent output pixels from being generated in regions where there are relatively few good pixels to contribute to the smoothed result.

By default, a null (!) value is used for WLIM, which causes the pattern of bad pixels to be propagated from the input image to the output image unchanged. In this case, smoothed output values are only calculated for those pixels which are not bad in the input image.

If a numerical value is given for WLIM, then it specifies the minimum fraction of good pixels which must be present in the smoothing box in order to generate a good output pixel. If this specified minimum fraction of good input pixels is not present, then a bad output pixel will result, otherwise a smoothed output value will be calculated. The value of this parameter should lie between 0.0 and 1.0 (the actual number used will be rounded up if necessary to correspond to at least one pixel). [!]

Results Parameters

SIGMA = _DOUBLE (Write)
The estimation of the RMS noise per pixel of the output image.

Examples:

ffclean dirty clean ∖
The NDF called dirty is filtered such that pixels that deviate by more than three standard deviations from the smoothed version of dirty are rejected. Three iterations are performed. Each pixel in the smoothed image is the average of the neighbouring nine pixels. The filtered NDF is called clean.
ffclean out=clean in=dirty thresh=[-100,200]
As above except only those pixels whose values lie between −100 and 200 can be cleaned.
ffclean poxy dazed [2.5,2.8] [5,5]
The two-dimensional NDF called poxy is filtered such that pixels that deviate by more than 2.5 then 2.8 standard deviations from the smoothed version of poxy are rejected. The smoothing is an average of a 5-by-5-pixel neighbourhood. The filtered NDF is called dazed.

Notes:

Related Applications

KAPPA: CHPIX, FILLBAD, GLITCH, MEDIAN, MSTATS, ZAPLIN; FIGARO: BCLEAN, COSREJ, CLEAN, ISEDIT, MEDFILT, MEDSKY, TIPPEX.

Implementation Status: