HDR: Automated Local Dynamic Range Optimization

A four-panel image showing different subtle uses of the HDR module in StarTools.
Example of subtle local dynamic range manipulations. Top left original. Top right 'Equalize' algorithm. Bottom left 'Optimize' algorithm. Bottom right 'Reval All' algorithm.

The HDR (High Dynamic Range) module optimises local dynamic range, in order to bring out the maximum amount of detail that is hidden in your data.

A HDR optimisation tool is a virtual necessity in astrophotography, owing to the huge brightness differences (aka 'dynamic range') innate to various objects that exist in deep space.

The HDR (High Dynamic Range) module optimises local dynamic range, in order to bring out the maximum amount of detail that is hidden in your data.

As opposed to other approaches (for example wavelet-based ones), the HDR module enhances dynamic range allocation locally (not just globally). It further takes into account psycho-visual theory (i.e. the way human vision perceives and processes detail) in the way the controls operate on the image.

Finally, the HDR module does not exacerbate noise grain like simpler dynamic range algorithms, factoring in noise propagation into the size of the final detail enhancement.

The result is an artefact free, totally natural looking image with real detail that does not suffer from the problems that other approaches suffer from, such as looking 'flat', looking too busy, or blowing out highlights such as stars.


usage

The HDR module optimises local dynamic range allocation for smaller details (e.g. on a more local level) than the Contrast module; the HDR module works primarily medium-to-small features in the image.

The HDR module complements the Sharpen module and is generally a more flexible and powerful alternative that generally achieves artifact-free results. Examples of use cases are bright galaxy cores where small detail is still recoverable in the highlights.

The HDR module does not exacerbate noise grain like simpler dynamic range algorithms, factoring in noise propagation into the size of the final detail enhancement. As such, it is meant after your non-linear dataset has been stretched, for example using the Development or AutoDev modules.

As with most modules in StarTools, the HDR module comes with a number of presets;

  • Optimise - accentuates detail
  • Equalise - pulls detail into the midtones and out of the shadows and highlights
  • Tame - pulls detail into the midtones and out of just the highlights
  • Reveal - reveals latent structural detail in the highlights (set 'Algorithm' to 'Reveal All' to also reveal structural detail in the shadows)

Going beyond the presets, more detailed adjustments can be made, starting with the 'Detail Size Range' parameter. This parameter is highly influential on the end result. It governs the range of detail sizes HDR should concentrate on, in order to bring out the most detail. Keeping this value small will see small detail accentuated. However, using larger values will see both small and large structural detail modified. Using larger values will progressively dig out larger scale structures and can be quite effective in highlighting these.

A selection of different algorithms to bring out detail exists. These are chosen through the 'Algorithm' parameter;

  • 'Equalize', much like the preset, pulls detail into the midtones and out of the shadows and highlights.
  • 'Tame highlights' uses the 'Equalize' algorithm to enhance just the highlights. It is a great tool for reducing glare, very effectively negating brightness build-up in DSO cores and galaxies. It can yield similar results to the Contrast module, but on smaller scales.
  • 'Brighten Dark' uses the 'Equalize' algorithm to enhance just the shadows. It just can be an extremely useful tool for bringing out latent detail in the shadows, such as faint, larger scale nebulosity. Because the Reveal module as whole factors in noise propagation into the size of the final detail enhancement, it does not tend to introduce much noise grain and will only bring out larger scale structures if detected.
  • 'Optimize soft' uses a fairly conservative detail enhancement strategy and is useful to give, for example, an image of a DSO a bit more 'punch' if it is mostly very wispy or shrouded in nebulosity.
  • 'Optimize hard' is a less conservative version of 'Optimize soft' and is a good general purpose structural detail enhancer.
  • 'Reveal DSO core' uses the 'Reveal' algorithm and applies it to just the highlights. It is a very aggressive, but also effective, structural detail hunter. Its aggressiveness can be controlled by the 'Strength' parameter. The Reveal algorithm is a (very, very) distant cousin of the simple Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm, but rather than performing local histogram equalisation, it performs local histogram stretching and not equalisation, thereby avoiding artifacts and noise grain exacerbation in areas with low signal-to-noise ratios. The 'Reveal DSO core' only workson the highlights.
  • 'Reveal All' is similar in all aspects to the 'Reveal DSO core' algorithm, with the exception that it is also applied to the shadows, enhancing the totality of the local dynamic range.

In order to throttle how much the shadows and highlights respond to the enhancements, a brightness mask is used, the power of which is controlled by the 'Dark/Bright Response' parameter.


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