Open an image stack ("dataset"), fresh from a stacker. Make sure the dataset was stacked correctly, as StarTools, more than any other software, will not work (or work poorly) if the dataset is not stacked correctly or has been modified beforehand. Your dataset should be as "virgin" as possible, meaning unstretched, not colour balanced, not noise reduced and not deconvolved. Please consult the "starting with a good dataset" section in the "links & tutorials" section.
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 always safe. The second option may yield even better results if certain conditions are met. Depending on what you choose here, StarTools may work exclusively on the luminance (mono) part of your image, bringing in color later; StarTools is able to seamlessly process color and detail separately (yet simultaneously).
Tracking is now engaged (the Track button is lit up green). This means that StarTools is now monitoring how your signal (and its noise component) is transformed as you process it.
Once imported, 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.
StarTools' AutoDev module uses image analysis to find the optimum custom curve for the characteristics of the data.
Human nor machine will be able to discern detail objectively or with certainty.
The first-pass algorithm is an enhanced wavelet denoiser, meaning that it is able to attenuate features based on their size.
You can convert everything you see to a format you find convenient. Give it a try!