Certain operations performed by IUESIPS may be categorized as screening or
preprocessing operations which prepare the data for subsequent processing.
The operations of this nature which are currently performed on IUE data are
described in this section.
Utilizing techniques developed at VILSPA by K. Northover (1980), the microphonics screening done by IUESIPS for all LWR images is based on the characteristics of the image data in the last 32 samples of each image line. This area is outside of the target region, where the pixel values are zero except for noise. With the IUESIPS program MICRO, lines affected by the microphonics are initially identified by applying a threshold to the variance of the last 32 samples; the interference amplitude is then estimated on the basis of the power spectrum of the sampled data, with successive image lines processed in pairs. Image lines with estimated noise amplitudes in excess of the chosen threshold (corresponding to a peak-to-peak noise amplitude of f 10 DN) are flagged by a notation in the image processing history portion of the label (see Section 9.3 ), which is subsequently used by other programs: the spectral registration routine (Section 6.3.2.1 ) uses this information to avoid areas affected by the microphonics, and the spectral extraction routines (Section 7 ) use this information to flag extracted fluxes derived from lines affected by the microphonic interference.
Long IUE exposures characteristically contain "bright spots", i.e., pixels with unusually high DN values which comprise discrete impulse noise often reaching the saturation level. Such bright spots are thought to be caused either by permanent blemishes in the target surface, by extraordinarily sensitive ("hot") pixels which result in recurrent bright spots at fixed locations, or by radiation-induced events within the UV converter which result in randomly placed, nonrecurrent bright spots (Ponz, 1980 a,b).
Ponz (1980 a,b) has described an algorithm for detecting in raw images bright spots on the basis of their limited spatial extent and unusual brightness values, primarily through a median filtering technique. The IUESIPS program BSPOT provided by VILSPA incorporates this algorithm to flag bright-spots as described below. At GSFC, this program was implemented on 19 November 1982.
Let DN (i,j) be the DN value of the pixel at line i, sample j. Further, let
AVE and MED represent operators which return the weighted average and median
values of their argument, respectively. Then the pixel at (i,j) is detected
as a bright spot if
is a DN threshold value, and (k,l) are positional elements of a
spatial window centered on the pixel at (i,j) and consisting of seven pixels
along the image diagonal (i.e., nearly along the dispersion direction). The
condition in equation
3-1 is included to reduce, in the general case, the
number of times the median operation in equation
3-2 is performed, thereby
saving computer time.
In practice, the spatial windows are weighted according to the weights (0, 0, 1, 0, 1, 0, 0), and a threshold value of del = 90 DN is employed. Future studies may examine the feasibility of parameterizing the threshold value according to the image background level, which could improve detectability of bright-spots on very high background images. The area of the image searched for bright-spots corresponds to that containing the spectral orders. Pixel locations detected as bright-spots are written to a disk data set subsequently read by the spectral extraction routines Section (7) so that extracted fluxes derived from bright-spot pixels may be flagged appropriately.
Ponz (1980 a,b) has published partial listings of recurrent bright spots in the SWP and LWR cameras which are reprinted here as Tables 3-1 and 3-2. The table entries include the line and sample positions both in raw and geometrically corrected (see Section 4) frames of reference and the approximate corresponding wavelengths for the various dispersion modes and apertures. The notation "B" means the background, rather than gross, spectrum is generally affected. Ponz estimates the expected error in wavelength for low dispersion to be 5 Å, and for high dispersion to be 0.3 Å. Double high dispersion wavelength entries in certain instances indicate that adjacent orders may be affected.
Imhoff (1984c) has provided positions of additional permanent belmishes in the LWR and LWP cameras, given in Table 3-3.
"Partial-read" images are those for which only a portion of the the target has been read. By not reading a full 768 x 768 array, a substantial fraction of the operations overhead time associated with the camera readout and subsequent preparation for next exposure is saved. Partial-read images are used only in the low dispersion mode and are always read out in a standard way so that a camera-dependent rectangular partial image, sufficient to encompass the entire region normally extracted in low dispersion processing (see Sections 5 and 7), is generated. Table 3-3 lists the standard parameters defining the partial-read areas in raw image space for the IUE cameras.
The partial-read images are preprocessed by using the program INSERT to imbed the partial-read area into a full 768 × 768 array for which DN values outside of the partial-read area are zero. This is done to enable the normal IUESIPS processing, which works on 768 x 768 images, to occur without further special consideration of the partial-read nature of the images; see also Section 5.3.
Note that the partial reads are being evaluated to insure that the process has no permanent damaging effects on the cameras. Thus they have not yet been routinely used for GO observations.
| Parameter | LWP | LWR | SWP | SWR |
|---|---|---|---|---|
| Starting Line | 99 | 73 | 36 | 135 |
| Starting Sample | 31 | 123 | 33 | 175 |
| Number of Lines | 528 | 528 | 528 | 480 |
| Number of Samples | 576 | 624 | 528 | 576 |
~ 2482 Å in order 93