Artifacts

Raw data from detector systems often contains artifacts originating from elements which have abnormal properties. Photographic emulsions and photocathodes can have dust or scratches while digital detectors (e.g. CCD and photodiode arrays) are affected by defects in the manufacturing process. Besides these imperfections in the detectors also cosmic ray events and electric disturbances can corrupt parts of the data. It is important to locate these gross errors to avoid that they degrade the correct data during the further reductions. Such bad pixels are either replaced by a local estimate or flagged as non-valid. Although the latter option is the most correct not all image processing systems are fully supporting the use of non-defined values (mostly due to programming and computer overheads).

Depending on the available data different methods are applied to detect and correct gross errors in the data. When only one frame is available artifacts are identified by their appearance; they are normally very sharp features. Most filter techniques assume that the image is oversampled so that the values in any region of a given small size can be regarded as taken from a random distribution. If the image is undersampled (i.e. the point spread functions is unresolved) it is impossible to distinguish between real objects and gross errors.

For a well sampled frame f_i,j non-linear digital filters are used giving the resulting frame r_i,j :

$$r_{i,j} = \left\{ \begin{array}{ll} {\cal E}_{i,j} (f_{i+m,j... ...geq \vert f_{i,j} - {\cal E}_{i,j} \vert, \end{array} \right.$$ (2.8)

where ${\cal E}_{i,j}$ is a local estimate for f_i,j. The modification level L may vary over the frame but is normally set to 5-10 times the dispersion $\sigma$ of the noise, to avoid modifying its distribution. The local estimate ${\cal E}_{i,j}$ may or may not include the original value f_i,j. The latter is an advantage if most faults only have a size of one pixel. The simplest estimator is the arithmetic mean. The main problem is that it depends linearly on the data values of the bad pixels. If a few pixels with very large errors are located in the region used for the estimate it may be effected so much that normal pixels are modified. By applying Equation 2.8 with a mean estimator iteratively, it is possible to reduce the dependency on gross errors. This procedure is called $\kappa \sigma$-clipping and was investigated by Newell (1979).

To avoid this problem more stable estimators are preferred such as the mode or median. Since the mode may neither exist nor be uniquely defined, the median is normally used (Tukey, 1971). The median filter can only detect artifacts if they occupy less than half of the filter size. Therefore, its size must be larger than two times the largest defect which should be removed and smaller than the smallest object to be preserved.

Another group of non-linear filters is based on recursive filters which uses the already filtered values for the estimator $\cal E$. In the one dimensional case a frame f_i is transformed to :

$$r_i = \left\{ \begin{array}{ll} {\cal E}_i ( r_{i-1},r_{i-2}... ...}\;\; L \geq \vert f_i - {\cal E}_i \vert \end{array} \right.$$ (2.9)

where r_i = f_i is assumed for $i = 1,2,\cdots,n$. The estimator can either be a linear expression (e.g. average or a low order extrapolation) or be based on the median as above. Due to the numeric feedback these filters are intrinsicly more unstable, however, by including a limit L which depends on f_i a useful filter can be constructed (Grosbøl, 1980).

  

Figure 2.1: A dark current CCD exposure with cosmic ray events which are removed with non-linear filters. (A) original, (B) 5*5 median filter, (C) 5*1 median filter, and (D) 5*1 recursive filter.

The main advantage of this filter type, compared to the median filter, is its capability to remove artifacts larger than its own size. Figure 2.1 shows a CCD dark current exposure with cosmic ray events. It can be seen that all artifacts can be removed using either a large median filter or a recursive filter while small median filters are unable to remove the larger events. When real features are present such as spectra in Figure 2.2 the non-linear filters may modify spectral lines.

  

Figure 2.2: Removal of cosmic ray events on a CCD spectral exposure with different techniques: (A) original, (B) $5 \times 1$ median filter, (C) $5 \times 1$ recursive filter and (D) stack comparison.

When more than two images of the same region are available, it is possible to compare the stack of pixels from the different exposures. The frames must be aligned and intensity calibrated before a comparison can be performed. Artifacts become more difficult to detect if an alignment, hence rebinning, is needed due to its smoothing effect. Thus, the stacking technique is best suited for removing cosmic ray events and electronic disturbances. Statistical weights must also be assigned to the individual images depending on exposure and signal-to-noise ratio. Outliers in the stack of pixel values are rejected either by comparing them to the median or by applying $\kappa \sigma$-clipping techniques (Goad, 1980). The resulting frame is then the mean of the remaining values. A set of CCD images of the galaxy A0526-16 are shown in Figure 2.3 including the resulting stacked image. By having different origins of the galaxy in the exposures the chip artifacts could also be removed. For comparison with non-linear filter techniques, Figure 2.2D shows removal of cosmic ray events from the spectral frame discussed above.

  

Figure 2.3: Removal of artifacts on CCD exposures (A,B,C) of the galaxy A0526-16 by stacking the frames yielding the combined image (D).