First order of business for using the Decon module, is to generate an inverted defect and singularity mask. This mask should contain all pixels we wish to deconvolve (green in the mask editor), and exclude all pixels that are not suitable (not green in the mask editor). Pixels that are not suitable are areas that contain aberrant data, no data, or data that is non-linear. Examples are hot pixels, dead pixels, defective sensors columns, over-exposing star cores or (more rare) highlights that have been non-linearly compressed by the sensor to fit into the dynamic range to prevent over-exposure. For your convenience, an AutoMask feature is available by means of the 'AutoMask' button (also launched upon opening the Decon module).
The AutoMask feature is able to generate a suitable mask in most cases by selecting 'Auto-generate mask'. As of StarTools 1.6, a more conservative 'Auto-generate conservative mask' option is also available which refrains from masking out detail in the highlights as much. The latter may be useful if your dataset is quite clean and your acquisition instrument has a good linear response throughout the dynamic range including into the highlights. Alternatively, you may also launch the Mask editor to create (or touch up) a mask yourself.
Deconvolution is extremely sensitive to aberrant data, as it relies on all data to be "real" and (originally) linear, in order to undo the specified blur in that area of the image. Letting Decon deconvolve any aberrant data greatly impacts the immediate vicinity being deconvolved and virtually always leads to significant artefacts being generated.
And all this is just what Tracking does for the deconvolution module.
The 'Regularization' in StarTools is automatically set to a baseline that should yield a good balance between detail recovery and artefact/noise suppression.
In the case of a 'Lunar/Planetary' image, reconstructed highlights are allocated additional dynamic range, as to not make them overexpose.
StarTools' Deconvolution module allows for recovering detail in seeing-limited and diffraction-limited datasets.
There is no one perfect solution, but rather a range of approximations to the "perfect" solution.
You can convert everything you see to a format you find convenient. Give it a try!