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

Deconvolution; an example

This remarkable feature is responsible for never-seen-before functionality that allows you to, for example, apply correct deconvolution to heavily processed data. The deconvolution module "simply" travels back in time to a point where the data was still linear (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.

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.

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

Deconvolution; an example that gets even better

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

Because you initiated deconvolution at a later stage than normally can be the case, the deconvolution module can take into account how you further processed the image after it normally should have been invoked. The deconvolution module now has knowledge about a future it normally is not privy to in any other software. Specifically, that knowledge of the future, tells it exactly how you stretched and modified every pixel - including its noise component - after the time its job should have been done.

200% zoomed crop input and output. Left: original, right: Decon result. Thanks to signal evolution Tracking, and despite stretching, local dynamic range optimization and noise presence, Decon is able to recover the finest details without introducing artifacts, or the need for support masks or manual intervention.

You know what really loves per-pixel noise component statistics like these? Deconvolution regularization algorithms! A regularization algorithm suppresses the creation of artefacts 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 sub-optimal local supports needed, no subjective luminance masks needed, no selective blending needed. There is no exaggerated noise grain, just enhanced detail; objectively better results, in less time, with less hassle.

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 - much smarter - with your hard-won signal. This is what StarTools is all about.