Usage

Left: original, middle: deconvolved image with appropriate settings, right: deconvolved image with ringing artifacts due to an inappropriate (too high) choice for the Radius parameter.

First order of business for using the Decon module, is to generate an inverted star mask. This mask should contain all pixels we wish to deconvolve, with the exception of 'singularities' (e.g. areas that contain aberrant or no data, such as hot, dead pixels, defective sensors columns or over-exposing star cores). 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 star 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 instrument has a good linear response throughout the dynamic range and even into the highlights. Alternatively, you may also launch the Mask editor to create (or touch up) a mask yourself.

The Deconvolution algorithm applies "blind" deconvolution. That is, it attempts to autonomously find a suitable Point Spread Function (PSF) that fits your image. he Point Spread Function can be thought of as the "blur" that deconvolution is required to "undo". Its starting point is the assumption that the Point Spread Function has a Gaussian profile (as is typically a good approximation for seeing-limited data).

The kernel size for this Gaussian profile is controlled by the 'Radius' parameter. A good rule of thumb is to increase this value until ringing artefacts become noticeable, and then back off a little. As of StarTools 1.6, an 'Enhanced Deringing' parameter is available than can further ameliorate ringing artefacts.

Uniquely, thanks to Tracking in StarTools, deconvolution cares not if the image has been stretched or heavily processed already. Top left: original, top right: immediate deconvolution, bottom left: heavy processing of original, bottom right: deconvolution of heavily processed original.

Deconvolution for planetary, solar and lunar imaging can be achieved as well by switching 'Image Type' to 'Lunar/Planetary'. The difference between 'Deep Space' and 'Lanr/Planetary' mode deconvolution is the way reconstructed highlights are treated. In the case of 'Deep Space', reconstructed highlights are assumed to overexpose and dynamic range of the entire image is not adjusted, while reconstructed highlights in the case of a 'Lunar/Planetary' image, reconstructed highlights are allocated additional dynamic range, as to not make them overexpose.

Finding an appropriate latent PSF in StarTools' Decon module is an iterative process. In general, more iterations, controlled by the 'Iterations' parameter, will yield a better result but will take longer to compute. More iterations will 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.

Understanding regularization in StarTools' Decon module

The 'Regularization' parameter controls the balance between newly recovered detail and noise grain propagation. Deconvolution is exceptionally sensitive to noise; without something to check whether newly recovered detail is indeed detail or artifact, the compounding effect of multiple iterations of deconvolving noise will end up a noisy, artefacting mess. Regularization in StarTools is automatically set to a baseline that should yield a good balance between detail recovery and artefact suppression. For exceptionally clean datasets, you may wish to deviate somewhat from the baseline to show more detail.

As opposed to any other software, regularization (and deconvolution as a whole) in StarTools is not fazed by heavily processed data, thanks to signal evolution Tracking. Regularization in StarTools is wholly driven by per-pixel SNR statistics, thereby avoiding artefact development in low SNR areas, while guaranteeing maximum detail in higher SNR areas. In fact, this ability may make applying deconvolution later in your processing a good idea, as Decon will have more "up to date" SNR statistics to work with. The closer your image is to completion, the more settled per-pixel SNR measurements will be. The latter settled SNR-measurements can than be taken into account by the Regularization algorithm to yield the most appropriate results for your image.