Noise reduction and grain shaping is performed in three stages.
The first-pass algorithm is an enhanced wavelet denoiser, meaning that it is able to attenuate features based on their size. Noise grain caused by shot noise (aka Poisson noise) - the bulk of the noise astrophotographers deal with - exists on all size levels, becoming less noticeable as the size increases. Therefore, much like the Sharp module, a number of scale sizes ('Scale n' parameters) are available to tweak, allowing the denoiser to be more or less aggressive when removing features deemed noise grain at different sizes. Tweaks to these scale parameters are generally not necessary, but may be desirable if - for whatever reason - noise is not uniform and is more prevalent in a particular scale.
Noise grain caused by shot noise (aka Poisson noise) - the bulk of the noise astrophotographers deal with - exists on all size levels, becoming less noticeable as the size increases.
Different to basic wavelet denoising implementations, the algorithm is driven by the per-pixel signal (and its noise component) evolution statistics collected during the preceding image processing. E.g. rather than using a single global setting for all pixels in the image, StarTools' implementation uses a different setting (yet centred around a user-specified global setting) for every pixel in the image.
The wavelet denoising algorithm is further enhanced by a 'Scale Correlation' feature parameter, which exploits common psychovisual techniques, whereby noise grain is generally tolerated better in areas of increased (correlated) detail.
The general strength of the noise reduction by the wavelet denoiser, is governed by the 'Brightness Detail Loss' and 'Color Detail Loss' for luminance (detail) and chrominance (colour) respectively.
The noise reduction solution in StarTools is based wholly around energy removal - that is attenuation of the signal and its noise components in different bands in the frequency domain - and avoids any operations that may add energy. It does not enhance edges, does not manipulate gradients, and does not attempt to reconstruct detail. These important attributes make its use generally permissible for academic and scientific purposes; it should never suggest details or features that were never recorded in the first place.
Any removed energy, is collected per pixel and re-distributed across the image in a second pass, giving the user intuitive control, via the 'Grain Dispersion' parameter, over a hard upper size limit beyond which grain is no longer smoothed out.
The 'Grain Equalization' parameter lets the user reintroduce removed noise grain in a modified, uniform way, that is; appearing of equal magnitude across the image (rather than being highly dependent per-pixel signal strength, stretches and local enhancements as seen in the input image).
The 'Grain Equalization' feature an acknowledgement of the "two schools" of noise reduction prevalent in astrophotography; there are those who like smooth images with little to no noise grain visible, and there are those who find a tightly controlled, uniform measure of noise grain desirable for the purpose of creating visual interest and general aesthetics (much like noise grain is added for a "filmic" look in CGI). The noise signature of the deliberately left-in noise, is precisely shaped to be aesthetically pleasing for precisely this purpose.
Lastly, it should be noted that the 'Grain Equalization' feature only shapes and re-introduces noise in the luminance portion of the signal, but not in the chrominance (color) portion of the signal.
With a suitable dataset, workflows in StarTools are simple, replicable and short.
The Color module is very powerful - offering capabilities surpassing most other software - yet it is simple to use.
The two aspects - colour and luminance - of your image are neatly separated thanks to StarTools' signal evolution Tracking engine.
You can convert everything you see to a format you find convenient. Give it a try!