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Quick Start Tutorial: a quick generic work flow

The icons in the top two panels roughly follow a recommended workflow when read left to right, top to bottom.
The icons in the top two panels roughly follow a recommended workflow when read left to right, top to bottom.

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.

While processing your first images with StarTools, it may help knowing that the icons in the top two panels roughly follow a recommended workflow when read top to bottom, left to right.


Step 1: Import, start Tracking

Open an image stack ("dataset"), fresh from a stacker. 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 as possible.

Do not use any software that may meddled with your 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 configured 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. 32-bit integer FITS files are preferable.

Counter-intuitively, a good stacker output will 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, you are 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. If your dataset comes straight from a stacker, the first option is safe. Tracking is now engaged (the Track button is lit up green).


Step 2: Inspect your dataset

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;

  • Stacking artefacts close to the borders of the image. These are dealt with in the Crop or Lens modules
  • Bias or gradients (such as light pollution or skyglow). These are dealt with in the Wipe module.
  • Oversampling (meaning the finest detail, such as small stars, being "smeared out" over multiple pixels). This is dealt with in the Bin module.
  • Coma or elongated stars towards one or more corners of the image. These can be ameliorated using the Lens module.


Step 3

Step 3: Prep

Fix the issues that AutoDev has brought to your attention;

  1. Ameliorate coma using the Lens module.
  2. Crop any remaining stacking artefacts.
  3. Bin the image up until each pixel describes one unit of real detail.
  4. Wipe gradients and bias away. Be very mindful of any dark anomalies - bump up the Dark Anomaly filter if dealing with small ones (such as dark pixels) or mask big ones out using the Mask editor. You may also wish to use a mask to mask out nebulosity if using high values for the two Aggressiveness parameters.


Step 4: Final global stretch

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. If your image shows very bright highlights, know that you can "rescue" them later on using, for example, the HDR module.


Step 5: Detail enhancement

Season your image to taste. Apply 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. As opposed to other software, however, you don't need to be as concerned with noise grain propagation; StarTools will take care of noise grain when you finally switch Tracking off.


Step 6: Color calibration

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.


Step 7: Final noise reduction, switching Tracking off

Switch Tracking off and apply noise reduction. You will now see what all the "signal evolution Tracking" fuss is about, as StarTools seems to know exactly where the noise exists in your image, snuffing it out. The most important parameters to tweak are Smoothness, in combination with Grain Dispersion.


Video

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.



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