There are a few simple, but important, do's and don'ts to prepare your dataset for post-processing in StarTools.
Learning how to use a new application is daunting at the best of times. And if you happen to be new to astrophotography (welcome!), you have many other things, acronyms and jargon to contend with too. Even if you consider yourself an image processing veteran, there are some important things you should know. That is because some things and best practices play a bigger role in StarTools than in other applications. By the same token, StarTools is also much more lenient in some areas than other applications.
Most advice boils down to making sure your dataset is as virgin as possible. Note that doesn't mean noise-free or even good, it just means you have adhered to all the conditions and best-practices outlined here, to the best of your abilities.
When learning how to process astrophotography images, the last thing you want to do, is learning all sorts of post-processing tricks and techniques, just to work around issues that are easily avoidable during acquisition or pre-processing. Fixing acquisition and pre-processing issues during post-processing, will never look as good, while you will also not learn much from this; it is likely whatever you learn and do to fix a particular dataset learn is likely not applicable to the next.
Conversely, if your dataset is clean and well calibrated according to best practices, you will find workflows much more replicable and shorter. In short, it is just a much better use of your time and efforts! You will learn much quicker and you will start getting more confident in finding your personal vision for your datasets - and that is what astrophotography is all about.
If practical, try a divide & conquer strategy, focusing on areas of data acquisition, pre-processing, and post-processing separately and in that order. Be mindful that success in conquering one stage is important to be able to achieve success in the stage that immediately follows it.
When we say StarTools requires the most virgin dataset you can muster, we really mean it! It means no procedures or modifications must be done by any other software - no matter how well-meaning. It means no gradient or light pollution removal, no color balancing, not even normalization (if not strictly necessary), and no pre-compositing of the channels. Signal evolution Tracking - the reason why StarTools achieves objectively better results than other software - absolutely requires it.
Some common problems in StarTools, caused by ignoring the check-list above;
The above are all easily avoided by good acquisition techniques, correct stacker settings, and proper calibration with flats and - optionally - darks and/or bias frames.
Thank you StarTools for turning my average photos into amazing ones.
'Brightness/Color detail loss' specifies a measure of allowed acceptable detail loss in order to reduce noise.
With a suitable dataset, workflows in StarTools are simple, replicable and short.
StarTools' Detail-aware Wavelet Sharpening allows you to bring out faint structural detail in your images.
StarTools' Deconvolution module allows for recovering detail in seeing-limited and diffraction-limited datasets.
You can convert everything you see to a format you find convenient. Give it a try!