Getting to grips with new software can be daunting, but StarTools was designed to make this as painless as possible. This quick, generic work flow will get you started.
Open an image. Processing in StarTools is easiest and will yield vastly better results if the data is as "virgin" as possible, meaning unstretched, not colour balanced, not noise reduced and not deconvolved. Best results are achieved with data that is as close to what the camera recorded (e.g. simple photon counts) as possible.
Do not use any software that may have meddled with the data prior to passing it to your stacking program. Avoid any pre-conversion tools or software that came with your camera. Make sure that any stacking software that you use is set up to perform as little processing to the data as possible. For example, if you use Deep Sky Stacker make sure that Per Channel Color Calibration and RGB Channels Calibration are set to 'no'. Also make sure that, in Deep Sky Stacker, the final file is saved with settings 'embedded', rather than applied.
Counter-intuitively, the output you will be after will, more often than not, have a distinct, heavy color bias with little or no apparent detail. Worry not; subsequent processing in StarTools will remove the color bias, while restoring and bringing out detail.. If, looking at the initial image leaves you wondering how on earth this will be turned into a nice picture, you are often on the right track.
Upon opening an image, the Tracking dialog will open, asking you about the characteristics of the data. Choose the option that best matches the data being imported.
Launch AutoDev to help inspect the data. Chances are that the image looks terrible, which is - believe it or not - the point. In the presence of problems in the data, AutoDev will show these problems until they are dealt with. Because StarTools constantly tries to make sense of your data, StarTools is very sensitive to artefacts, meaning anything that is not real celestial detail (such as stacking artefacts, dust donuts, gradients, terrestrial scenery, etc.). Just 'Keep' the result. StarTools, thanks to Tracking, will allow us to redo the stretch later on.
At this point, things to look out for are;
Fix the issues that AutoDev has brought to your attention;
Once all issues are fixed, launch AutoDev again and tell it to 'redo' the stretch. If all is well, AutoDev will now create a histogram stretch that is optimised for the "real" object(s) in your clean data. If your data is very noisy, it is possible AutoDev will optimise for the noise, mistaking it for real detail. In this case you can tell it to Ignore Fine detail.
If your object(s) reside on an otherwise uninteresting or "empty" background, you can tell AutoDev where the interesting bits of your image are by clicking & dragging a Region Of Interest.
Don't worry about the colouring just yet - focus getting the detail out of your data first.
Season your image to taste. Apply some deconvolution with the Decon module, dig out detail with the Wavelet Sharpen ('Sharp') module, enhance Contrast with the Contrast module and fix any dynamic range issues with the HDR module.
There are many ways to enhance detail to taste and much depends on what you feel is most important to bring out in your image.
Launch the Color module.
See if StarTools comes up with a good colour balance all by itself. A good colour balance shows a good range of all star temperatures, from red, orange and yellow through to white and blue. HII areas will tend to look purplish/pink, while galaxy cores tend to look yellow and their outer rims tend to look bluer.
Green is an uncommon colour in outer space (though there are notable exceptions, such as areas that are strong in OIII such as the core of M42). If you see green dominance, you may want to reduce the green bias. If you think you have a good colour balance, but still see some dominant green in your image, you can remove the last bit of green using the 'Cap Green' function.
Switch Tracking off and apply noise reduction. You will now see what all the fuss is about, as StarTools seems to know exactly where the noise exists in your image and snuffs it out. The main parameters to tweak are 'Smoothness', 'Brightness Detail Loss' and 'Color Detail Loss'.
Pour yourself your favourite beverage and pat yourself on the back for a job well done!
A video is also available that shows a simple, short processing workflow of a real-world, imperfect dataset.
Please refer to the video description below the video for the source data and other helpful links.
Thank you StarTools for turning my average photos into amazing ones.
This is effectively signal processing in three dimensions; X, Y and time (X, Y, t).
Results are free to publish, as long as they are credited "Image acquisition by Jim Misti".
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