De-Noise: Detail Aware Wavelet-based Noise Reduction
The De-Noise module offers detail-aware, astro-specific noise reduction, which, paired with StarTools' Tracking feature, yields results that have no equal.
Whereas generic noise reduction routines and plug-ins for terrestrial photography are often optimised to detect and enhance geometric patterns and structures in the face of random noise, the De-Noise module is optimised to do the opposite and optimise patterns and structures that are non-geometric in nature in the face of random noise (as well as read noise).
When used in conjunction with StarTools' 'Tracking' feature which data mines every decision and noise evolution per-pixel during the user's processing, the results that De-Noise is able to deliver autonomously are absolutely unparalleled. The extremely targeted noise reduction that is provided in this case, can only be approximated in other software by spending many hours creating a noise mask by hand.
Denoising starts when switching Tracking off. It is therefore generally the last step, and for good reason. Being the last step, Tracking has had the longest possible time to track and analyse noise propagation. It therefore has the best and most accurate statistics available and can therefore achieve the best results on your behalf.
The first stage of noise reduction involves the selection of 3 subtly different noise reduction algorithms, and helping StarTools establish a visual base line for the noise grain. To establish this baseline, increase the 'Grain size' parameter until no noise grain of any size can be seen any longer. StarTools will use this baseline to more intelligently redistribute the energy in the various bands that is taken out during the wavelet denoising in the second stage. Note that this parameter is also still available for modification in the second stage, though it lacks the visual aid presented here.
After clicking 'Next', the wavelet scale extraction starts, upon which, after a short while, the second interactive noise reduction stage interface is presented.
The base algorithm that performs noise removal is an enhanced wavelet denoiser, meaning that it is able to remove features (such as noise) based on their size. Noise grain caused by shot 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 are available to tweak, allowing the denoiser to be more or less aggressive when removing features deemed noise grain at different sizes.
Some astrophotographers prefer to leave in a little noise at the lowest scale(s) to avoid an overly smooth image, though the algorithm in StarTools already tends to avoid oversmoothing due to its correlation feature.
The parameters that govern global noise reduction response (rather than per-feature-size) are 'Brightness/Color detail loss' and 'Smoothness'.
'Brightness/Color detail loss' specifies a measure of allowed acceptable detail loss in order to reduce noise. In color images, the 'Color detail loss' parameter works solely on any color noise, while the 'Brightness detail loss' parameter works on the detail itself, but not its colors.
The 'Smoothness' parameter determines how much (or little) the denoiser should take notice of any inter-scale detail correlation. Detail correlation is higher in areas that look 'busy' such as galaxy or nebula cores or shock waves, whereas detail correlation is low in areas that are 'tranquil' such as opaque homogenous gas clouds. Increasing 'Smoothness' progressively ignores such correlation, allowing for more aggressive noise reduction in areas of higher correlation.
'Scale correlation' specifies how deep the denoiser should look for detail that may be correlated across scales. Most data can withstand deep correlation, however some types of data may exhibit an artificially introduced correlation. This can be the case with data that;
- has been drizzled with insufficient frames
- originates from a sensors with a color filter array (for example an OSC or DSLR) and where insufficient frames were stacked
- was not sufficiently dithered between sub-frame acquisition
- has any other type of recurring embedded pattern, visible or latent
Noise in such cases will not exhibit a Poission distribution (e.g. it does no longer resemble shot noise) and will exhibit correlation in the form of clumps or streaks. Such data may require a shallower 'Scale correlation' value. More generally, such types of noise/artefacts are beyond the scope of the denoise module's capabilities and should be corrected during acquisition and pre-processing, rather than at the post-processing stage.
You may also be interested in...
- Zooming, panning and scaling under Interface
StarTools implements a custom scaling algorithm in its user interface, which makes sure that perceived noise levels stay constant, no matter the zoom level.
- Bin: Trade Resolution for Noise Reduction under Modules
Once detail no longer fits in a single pixel, but instead gets "smeared out" over multiple pixels due to atmospheric conditions (resulting in a blur), binning may turn this otherwise useless blur into noise reduction.