Masks for Deconvolution

Descriptions of mask types and how to create them

For the most careful imaging, you will want to restrict the region over which you allow CLEAN components to be found by using a mask. This mask is generally referred to as a clean mask.

Creating a clean mask:

There are several different ways to specify a clean mask, including:

  1. A text-based region. The CASA region text format can be used to define clean regions either by specifying the region directly in the tclean/clean command or by using an ASCII text file containing the specifications. You can use the viewer to save a region formatted according to the CRTF specification. To do this, an image must already exist to serve as a reference or template to create the mask image or the region.
  2. An image consisting of only 1 (valid) and 0 (invalid) pixel values. Such images can be generated or modified using tasks such as makemask. The mask has to have the same shape (number of pixels, velocity, and Stokes planes) as the output image. An exception are single velocity and/or single Stokes plane masks. They will be expanded to cover all velocity and/or Stokes planes of the output cube. 
  3. An automatically generated mask (tclean only). There are several experimental algorithms available in tclean for automatically masking emission during the deconvolution cycle. See the automasking section below for more details.
  4. A mask created by tclean/clean while interactively cleaning using the viewer. You can combine this method with the options above to create an initial clean mask and modify it interactively. 

However they are created, the masks are all converted (as necessary) and stored as CASA images consisting of the pixel values of 1 and 0. When mask files are read in and have undefined values, these values are treated as 1s by CASA. Mixed types of masks can be specified in the clean and tclean tasks. 

In CASA, the term, 'mask' for an image is used in two different contexts. One is used for CASA images/image analysis is a T/F mask (pixel mask), which can be embedded in the parent CASA image.  The 'mask' used in imaging normally refers to a 1/0 image, which is directly used to define deconvolution region(s) (or set a 'clean mask') in the tclean or clean tasks.

Automasking

The tclean task has an option to generate clean masks automatically during the deconvolution process by applying flux density thresholds to the residual image. Currently  "auto-multithresh" is the automasking algorithm available in tclean. Preveously available experimental alogrithms, "auto-thresh" and "auto-thresh2" were removed in CAS 5.4. The "auto-multithresh" algorithm can be selected via the usemask parameter. For this algorithm, the mask will be updated at the beginning of a minor cycle based on the current residual image. The algorithm uses multiple thresholds based on the noise and sidelobe levels in the residual image to determine what emission to mask. It also have functionality to remove ("prune") small mask regions that are unlikely to be real astronomical emission. More detailed descriptions of the algorithm are given below.

"auto-multithresh"

This algorithm is intended to mimic what an experienced user would do when manually masking images while interactively cleaning. The parameters sidelobethreshold and noisethreshold control the initial masking of the image. The sidelobethreshold indicates the minimum sidelobe level that should be masked, while the noisethreshold  indicates the minimum signal-to-noise value that should be masked. The threshold used for masking is the greater of the two values calculated for each minor cycle based on the rms noise and sidelobe levels in the current residual image. 

Regions smaller than a user-specified fraction of the beam can be removed, or "pruned", from the mask. The size of the region is defined as the number of contiguous pixels in the region. The figure below shows an example of the pruning process.


Type Figure
ID masks-for-deconvolution-fig-prune
Caption Figure 1 - An example of the pruning process. The image on the left shows the original threshold mask, while the image on the right shows the resulting mask after all regions smaller than a user-specified fraction of the beam area have been removed. 


The resulting masks are all convolved with a Gaussian that is a multiple of the synthesized beam size, which is controlled by the parameter smoothfactor. Only values above some fraction of the smoothed Gaussian peak are retained, which is defined via the cutthreshold parameter. Note that cutthreshold is defined as a fraction of the smoothed Gaussian peak, not as an absolute value. This procedure ensures that sources are not masked too tightly, i.e., there is a margin between the emission and the mask.  Note that smoothfactor and cutthreshold are related. A large smoothfactor and high cutthreshold can give a similar region to a lower smoothfactor but lower cutthreshold. Note that setting the cuttreshold too high (>~0.2) will tend to remove faint regions. 

An image showing a threshold mask, a smoothed threshold image, and the final smoothed threshold mask created by cutting above particular fraction of the peak threshold..

Type Figure
ID masks-for-deconvolution-fig-smooth-and-cut
Caption Figure 2 - An example of the process used to ensure that sources are not masked too tightly. The left hand image shows the initial threshold mask. The middle image shows the threshold mask convolved with a Gaussian. The right image shows the final threshold mask where only emission above some fraction of the peak in the smoothed mask is retained. The final mask is larger than the original threshold mask and better encapsulates the emission.

The initial threshold mask can be expanded down to lower signal-to-noise via binary dilation. This feature is particularly useful when there is significant faint extended emission. The lownoisethreshold parameter is used to create a mask of the low signal-to-noise emission, which we refer to as the constraint mask. Th previous total positive mask is expanded (or grown) via an operation known as binary dilation, which expands each mask region using a structuring element (also known as a kernel). Currently the structuring element is fixed with a 3x3 matrix with the diagonal element being 0. We use a constraint mask based on a low signal-to-noise threshold to limit the expansion of the mask to regions within the lownoisethreshold. Only the regions in the constraint mask that touch the previous mask are retained in the final constraint mask. Then the final constraint mask is pruned, smoothed, and cut using the same method as the initial threshold mask. 

The sub-parameter growiterations gives a maximum number of iterations used to "grow" the previous masking into the low signal-to-noise mask, which can speed up masking of large cubes at the expense of possibly undermasking extended emission. The sub-parameter dogrowprune can be used to turn off pruning for the constraint mask, which also may also speed up this process.

Type Figure
ID masks-for-deconvolution-fig-grow
Caption

Figure 3 - An example of how the masks are expanded into low signal-to-noise regions. The top row shows the binary dilation process. Left: The low signal-to-noise threshold mask used as a constraint mask. Middle: The final mask from the previous clean cycle. Right: The result of binary dilating the mask from the previous clean major cycle into the constraint mask. The bottom left image shows the binary dilated mask multiplied by the constraint mask to pick out only those regions in the constraint mask associated with the previous clean mask. The bottom middle image shows the final pruned, smoothed, and cut mask.

There is also an experimental absorption masking feature controlled by the sub-parameter negativethreshold, which has an analogous definition to noisethreshold. This feature assumes that the data has been continuum subtracted. Absorption masking can be turned off by setting the negativethreshold vaue to 0 (the default). Note that the negative and positive threshold masks are tracked separately and that the negative mask is not pruned or expanded into lower signal-to-noise regions.

Finally, all the masks (initial threshold mask, negative mask, low noise threshold mask) are added together with the mask from the previous major cycle to form the final mask.

All the operations described above, including obtaining image statistics, are done per spectral plane for spectral line imaging. If a channel is masked using the noise threshold and the resulting final mask is zero, then future auto-masking iterations will skip that channel. The minpercentchange parameter is an experimental parameter that controls whether future masks are calculated for a particular channel if the mask changes by less than n% after major cycle where the cyclethreshold is equal to the threshold for the clean. In general, we recommend minpercentchange to be set to -1.0 (turned off).

The verbose parameter records information to the log on whether a channel is included in the masking, the image noise and peak, the threshold used and it's value, the number of regions found in the initial mask and how many were pruned, the number of region found in the low noise threshold mask and how many of those are pruned, and the number of pixels in the negative mask. This information is helpful for optimizing parameters for different imaging cases as well as general debugging.

Algorithm In Detail

  1. Calculate threshold values based on the input parameters.
    1. sidelobeThresholdValue = sidelobeThreshold * sidelobeLevel * peak in residual image
    2. noiseThresholdValue =  noiseThreshold * rms in residual image
    3. lowNoiseThresholdValue = lowNoiseThreshold * rms in residual image
    4. negativeThresholdValue = negativeThreshold * rms in residual image
  2. Create the threshold mask.
    1. maskThreshold = max(sidelobeThresholdValue,noiseThresholdValue)
    2. Create threshold mask by masking all emission greater than maskThreshold.
    3. Prune regions smaller than minBeamFrac times the beam area from threshold mask.
    4. Smooth the mask image by smoothFactor * (beam size).
    5. Mask everything above cutThreshold * the peak in the smoothed mask image.
  3. Expand the mask to low signal-to-noise.
    1. lowMaskThreshold = max(sidelobeThresholdValue,lowNoiseThresholdValue)
    2. Create constraintMask by masking all emission greater than lowMaskThreshold.
    3. Use binary dilation expand the previous clean cycle mask into the constraintMask.
    4. Create the low S/N mask by retaining only the regions in the constraintMask that are connected to the previous clean cycle mask.
    5. Prune [can turn this off with dogrowprune=False], cut, and smooth the low S/N mask the same way as was done for the threshold mask.
  4. Mask the absorption (experimental)
    1. If negativethreshold >0.0:
      1. negativeMaskThreshold =  -  max(negativeThresholdValue, sidelobeThresholdValue)
      2. mask negative pixels with values <= negativeThresholdValue
      3. Cut and smooth the absorption mask the same way as was done for the threshold mask.
  5. Add the threshold mask, the low S/N mask, the absorption mask, and the mask from previous clean cycle together.

 

A Note on Input Parameters

The default "auto-multithresh" parameters have been optimized for the ALMA 12m array in its more compact configurations (75 percentile baseline < 300m). The parameters may need to be modified for other input cases, e.g., ALMA 12m long baseline data, ALMA 7m array data, and  VLA data. The main parameters to explore are noisethreshold, sidelobethreshold, lownoisethreshold, minbeamfrac, and negativethreshold (if you have absorption). We do not recommend changing the cutthreshold and smoothfactor parameters from their default values. The dogrowprune and growiterations parameters are primarily used to improve the speed of the algorithm for large cubes.

 

Citation Number 1
Citation Text Williams, de Geus, and Blitz (1994) (ADS)