Stage one

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.

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.