In StarTools, Histogram Transformation Curves are considered obsolete. AutoDev uses image analysis to achieve better results in a more intuitive way.
When data is acquired, it is recorded in a linear form, corresponding to raw photon counts. To make this data suitable for human consumption, stretching it non-linearly is required.
Historically, simple algorithms were used to emulate the non-linear response of photographic paper by modelling its non-linear transformation curve. Later, in the 1990s because dynamic range in outer space varies greatly, "levels and curves" tools allowed imagers to create custom histogram transformation curves that better matched the object imaged so that the most amount of detail became visible in the stretched image.
StarTools' AutoDev module however uses image analysis to find the optimum custom curve for the characteristics of the data.
Creating these custom curves was a highly laborious and subjective process. And, unfortunately, in many software packages this is still the situation today. The result is almost always sub-optimal dynamic range allocation, leading to detail loss in the shadows (leaving recoverable detail unstretched), shrouding interesting detail in the midtones (by not allocating it enough dynamic range) or blowing out stars (by failing to leave enough dynamic range for the stellar profiles).
StarTools' AutoDev module however uses image analysis to find the optimum custom curve for the characteristics of the data. By actively looking for detail in the image, AutoDev autonomously creates a custom histogram curve that best allocates the available dynamic range to the scene, taking into account all aspects and detail. As a consequence, the need for local HDR manipulation is minimised.
AutoDev is, in fact, so good at its job that it is also one of the most important tools in StarTools for initial data inspection; using AutoDev as one of the first modules on your data will see it bring out problems in the data, such as stacking artefacts, gradients, bias, dust donuts, etc. Upon removal and/or mitigation of these problems, AutoDev may then be used to stretch the cleaned up data.
AutoDev has a lot of smarts behind it. It analyses a Region of Interest ("RoI") - by default the whole image - so that it can find the optimum histogram transformation curve based on what it sees. The 'Develop' module by comparison, is more simple in that it mimics photographic film development, which doesn't actually take into account what is in the image.
Understanding AutoDev is pretty simple really; its job is to look at what's in your image and to make sure as much as possible is visible. The problem with a histogram transformation curve (aka 'global stretch') is that it affects all pixels in the image. So, what works in one area (bringing out detail in the background), may not necessarily work in another (for example, it may make a medium-brightness DSO core harder to see). Therefore stretching the image is always a compromise. AutoDev finds the best compromise global curve, given what detail is visible in your image and your preferences. Of course, fortunately we have other tools like the Contrast and HDR modules to 'rescue' all detail by optimising for local dynamic range on top of global dynamic range.
AutoDev finds the best compromise global curve, given what detail is visible in your image and your preferences.
The latter is a really useful feature, as it is also very adept at finding artefacts or stuff in your image that is not real detail but requires attention. That's why AutoDev is also extremely useful to launch as the first thing after loading an image to see what - if any - issues need addressing before proceeding. If there are any, AutoDev will show them to you guaranteed.
After fixing such issues, we can start using AutoDev's skills for showing the remaining (this time real celestial) detail in the image.
If most of the image consists of a background and just a small object of interest, by default AutoDev will weigh the importance of the background higher (since it covers a much larger part of the image vs the object); given what it has to work with it's the best compromise. If the background is noisy, it will start digging out the noise, mistaking it for fine detail. If this behaviour is undesirable, there are a couple of things you can do in AutoDev.
You'll find that, as you include more background around the object, AutoDev, as expected, starts to optimise more and more for the background and less for the object; it's doing its job very well!
So, to use the ROI effectively, give it a 'sample' of the important bit of the image. This can be a whole object, or it can be just a slice of the object that is a good representation of what's going on in the object in terms of detail, for example a slice of a galaxy from the core, through the dust lanes, to the faint outer arms.
There is no shame in trying a few different ROIs in order to find one you're happy with. What ever the case, it certainly beats pulling histogram curves, both in results and objectivity (you've got a dedicated algorithm/assistant watching over your shoulder!).
Automated black and white point detection ensures your signal never clips, while making histogram checking a thing of the past.
If your data is very noisy, it is possible AutoDev will optimise for the noise, mistaking it for real detail.
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