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bottletopburly

New startools tutorials

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Posted (edited)

Ivo has released some tutorials on startools, I haven’t watched them myself  yet, on the to do list for later, but here are the links @jager945

 

Edited by bottletopburly
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I've watched the first one and it's a good guide and gets a good result with little over fiddling on the data I used.

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Posted (edited)

Thanks for spreading the word! 😀

I'm really hoping the Tracking video helps people understand better what is so incredibly special about ST's processing engine vs traditional applications.

Tracking is actually where most of my time, efforts and R&D are spent. The "time-travel" of your signal is why StarTools can/should yield better results with the same data.

There are some significant improvements coming up to the Decon module (again!), making even better use of "future knowledge" about your signal.

I try to explain it on the website;

Quote

Because in StarTools you initiate 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 (it certainly isn't in other applications). 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 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". It is like going back in time a week and telling yourself the lottery numbers to today's draw.

 

Edited by jager945
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Personally I found startools quite easy to get a result from  but where I struggled at first was because my data wasn’t very good in the first place , so Astrophotography was all new to me my data was poor , my processing skills was a challenge , I found photoshop cumbersome     and to much faffing about , what did help after using startools and reading watching various tutorials was using better Data files to practice on from https://groups.yahoo.com/neo/groups/dslr_astro_image_processing/info  that made learning easier  and now I’m guiding and dithering and getting flats my data is improving and startools can give me a good result I’m happy with in a fairly short time .So thanks @jager945👍

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Posted (edited)

@bottletopburly You're so right. It's kind of unfair for newbies; there is so much to tackle and if acquisition is poor, then post-processing is so much harder. It's a double whammy. What you did was the perfect approach; divide and conquer. i.e. decoupling learning post-processing from learning data acquisition.

Perhaps not a video/tutorial, but I thought I'd post this animated GIF here as well. It shows what the years of development between 1.3.5 and 1.4 (to be released soon) have yielded in terms of signal fidelity.

It's a 400% enlarged crop of a Jim Misti M8 Hydrogen-alpha dataset that has been intentionally non-linearly "mangled" to put Tracking through its paces.
Specifically, it has been stretched, Contrast enhanced, then linearly deconvolved (using Tracking time travel and precognition of future signal evolution), then noise reduced (also using Tracking).
Workflow, parameters and settings were kept identical between 1.3.5 and 1.4. The only difference is the algorithms in Decon and Denoise making increasingly more sophisticated use of the Tracking data/time travel over the years (with one unreleased quality bump applied here that hasn't been released yet).

spacer.png

("Original" is signal as visible, without any Tracking-enabled modules applied yet)

Edited by jager945
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