The Deconvolution algorithm's task, is to reverse the blur caused by the atmosphere and optics. Stars, for example, are so far away that they should really render as single-pixel point lights. However in most images, stellar profiles of non-overexposing stars show the point light "smeared" out, yielding a core surrounded by light tapering off. Further diffraction may be caused by spider vanes and/or other obstructions in the Optical Tube Array, for example yielding diffraction spikes.
The point light's energy is scattered around its actual location, yielding the blur. The way a point light is blurred like this, is also called a Point Spread Function (PSF). Deconvolution is all about modelling this PSF, then finding and applying its reverse to the best of our abilities.
Atmospheric or lens-related blur is more easily modelled, as its behaviour and effects on long exposure photography has been well studied over the decades. 5 subtly different models are available for selection via the 'Primary Point Spread Function' parameter;
The size (aka 'kernel size') of the chosen 'Primary Point Spread Function' is controlled by the 'Primary Radius' parameter. A good rule of thumb is to increase this value until ringing artefacts become noticeable, and then back off a little until it disappears again. An 'Enhanced Deringing' parameter is available than can further ameliorate ringing artefacts.
Converging on an optimal solution is an iterative process in the Deconvolution module. In general, more iterations, controlled by the 'Iterations' parameter, will yield a better result but will take longer to compute. More iterations tend to yield diminishing returns. Different datasets may benefit from more or fewer iterations. You may wish to experiment on a smaller preview section to evaluate improvements before computing deconvolution of the entire image. Deconvolution in StarTools always converges on an optimal solution and does not destabilise as seen in other software, except when 'Error Diffusion' is set to a non-zero value.
A 'Secondary Point Spread Function' may be specified by clicking on a guide star. The Deconvolution module will then use the star as a guide to construct a suitable total PSF. Good star samples are stars that do not overexpose, but are not too dim, are closer to the center of the image and have a flat background. When a 'Secondary Point Spread Function' is provided, the total/final PSF used is a combination of that PSF modulated by the 'Primary Point Spread Function'. This allows you to create a final PSF that is tightly controlled by the ideal atmospheric profile (and its radius) as specified by the 'Primary Point Spread Function', while exhibiting a custom measure of deformity as seen in the selected star's PSF.
For example, to make Decon use the 'Secondary Point Spread Function' only, set the 'Primary Point Spread Function' to 'Circle of Confusion (No Atmosphere)' and specify a very large 'Primary PSF Radius'. As expected, smaller radii will start cutting off the 'Secondary Point Spread Function' in a circular fashion. For a gentler tapering off of the 'Secondary Point Spread Function', you can use, for example,a 'Gaussian (Fast)' profile for the 'Primary Point Spread Function'.
Uniquely, any star chosen as a 'Secondary Point Spread Function' can be made to iteratively deconvolve along with the image (by choosing one of the 'Dynamic' Star Sample settings). This effectively means that the deconvolution process deconvolves with an ever-changing total PSF. This mode can yield very good, even superior results, depending on the fidelity of the initial star sample. If this mode is selected and you are using a preview, make sure that the chosen star is included in the preview and falls well into the preview area - a message will be shown if this is not the case.
When choosing a star as PSF guide star and wishing to use a Dynamic Star Sample feature, make sure that the star is not masked out; masked out pixels are displayed in red if a guide star is set. Masked out stars (and thus their derivative PSF) will not iteratively deconvolve along with the image and are hence not suitable for this mode.
StarTools' Deconvolution module allows for recovering detail in seeing-limited and diffraction-limited datasets.
The latter settled SNR-measurements can than be taken into account by the Regularization algorithm to yield the most appropriate results for your image.
The deconvolution module now has knowledge about a future it normally is not privy to in any other software.
It is important to understand two things about deconvolution; Deconvolution is "an ill-posed problem", due to the presence of noise in every dataset.
Deconvolution of planetary, solar and lunar images can be achieved as well by switching 'Image Type' to 'Lunar/Planetary'.
You can convert everything you see to a format you find convenient. Give it a try!