The power of 3D (X, Y, t) signal processing

Correct deconvolution of extremely noisy, stretched, locally contrast-enhanced (top left) data. No further masks (other than simple auto-generated star mask), local supports or selective processing was performed. Noise grain is correctly identified and ignored. Only areas with sufficient SNR are enhanced.

Deconvolution; an example

This remarkable feature is responsible for never-seen-before functionality that allows you to, for example, apply deconvolution to heavily processed data. The deconvolution module "simply" travels back in time to a point where the data was still linear (normally deconvolution can only correctly be applied to linear data!). Once travelled back in time, deconvolution is applied and then Tracking forward-propagates the changes. The result is exactly what your processed data would have looked like with if you had applied deconvolution earlier and then processed it further.

Sequence doesn't matter anymore, allowing you to process and evaluate your image as you see fit. But wait, there's more!

A side-by-side image of M42's core
Signal evolution Tracking allows for many more enhancements over traditional software. For example color constancy (right), effortlessly visualizing features with similar chemical/phsycial properties, regardless of brightness.

Deconvolution; an example that gets even better

Time traveling like this is very useful and amazing in its own right, but there is another major, major difference in StarTools' deconvolution module.

The major difference, is that, because you initiated deconvolution at a later stage, the deconvolution module can take into account how you processed the image after the moment deconvolution should normally have been invoked (e.g. when the data was still linear). In a sense, the deconvolution module now has knowledge about a future it should normally never have been privy to. Specifically, that future tells it exactly how you stretched and modified every pixel after the time its job should have been done, including pixels' noise components.

You know what really loves per-pixel noise component statistics like these? Deconvolution regularization algorithms! A regularization algorithm suppresses the creation of artifacts caused by the deconvolution of - you guessed it - noise grain. Now that the deconvolution algorithm knows how noise grain will propagate in the "future", it can take that into account when applying deconvolution at the time when your data is still linear, thereby avoiding a grainy "future", while allowing you to gain more detail. It is like going back in time and telling yourself the lottery numbers to today's draw.

What does this look like in practice? It looks like a deconvolution routine that just "magically" brings into focus what it can. No local supports, luminance masks, or selective blending needed. No exaggerated noise grain, just enhanced detail.

And all this is just what Tracking does for the deconvolution module. There are many more modules that rely on Tracking in a similar manner, achieving objectively better results than any other software, simply by being smarter with your hard-won signal.


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