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!).
StarTools' Deconvolution module allows for recovering detail in seeing-limited data sets that were affected by atmospheric turbulence.
It doesn't stop there however – the Fractal Flux module can use any output from any other module as input for the flux to modulate.
'Brightness/Color detail loss' specifies a measure of allowed acceptable detail loss in order to reduce noise.
The HDR module optimises local dynamic range, in order to bring the maximum amount of detail that is hidden in your data.
The Contrast module optimizes local dynamic range allocation, resulting in better contrast, reducing glare and bringing out faint detail.
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