To be able to detect detail, AutoDev has a lot of smarts behind it. Its main detail detection algorithm 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".
Understanding AutoDev on a basic level is pretty simple really; its goal is to look at what's in your image and to make sure as much as possible is visible, just as a human would (try to) look at what is in the image and approximate the optimal histogram transformation curves using traditional tools.
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 it is important to understand that - fundamentally - globally stretching the image is always a compromise. AutoDev's job then, is to find 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, Sharp and HDR modules to 'rescue' all detail by optimising for local dynamic range on top of global dynamic range.
AutoDev's job then, is to find the best-compromise global curve, given what detail is visible in your image and your preferences.
Being able to show all things in your image equally well, is a really useful feature, as it is also very adept at finding artefacts or stuff in your image that is not real celestial detail but requires attention. That is 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 is virtually guaranteed to show them to you. After fixing such issues (for example using Crop, Wipe, Band or other modules), we can go on to use 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). This is understandable and neatly demonstrates its behavior. It will always look for the best compromise stretch to show the entire Region of Interest ("RoI" - by default the entire image). This also means that if the background is noisy, it will start digging out the noise, taking it as "fine detail" that needs to be "brought out". If this behaviour is undesirable, there are a couple of things you can do in AutoDev.
You will 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. 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. You can, for example, use 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, the result will be more optimal and objective than pulling at histogram curves.
There are two ways of further influencing the way the detail detector "sees" your image;
In AutoDev, you're controlling an impartial and objective detail detector, rather than a subjective and hard to control (especially in the highlights) bezier/spline curve.
Having something impartial and objective taking care of your initial stretch is very valuable, as it allows you to much better set up a "neutral" image that you can build on with the other local detail-enhancing tools in your arsenal (e.g. Sharp, HDR, Contrast, Decon, etc.). For example, when using Autodev, it will quickly become clear that point lights and over-exposed highlights, such as the cores of bright stars, remain much more defined. The dreaded "star bloat" effect is much less pronounced or even entirely absent, depending on the dataset.
However, knowing how to effectively use Region of Interests ("RoI") is crucial to making the most of AutoDev. Particularly if the object of interest is not image-filling, a Region of Interest will often be necessary. Fortunately the fundamental workings of the RoI are easy to understand.
Let's say our image is of galaxy, neatly situated in the center. Then confining the RoI progressively to the core of the galaxy, the stretch becomes more and more optimised for the core and less and less for the outer rim. Conversely, if we want to show more of the outer regions as well, we would include those regions in the RoI.
Shrinking or enlarging the RoI, you will notice how the stretch is optimised specifically to show as much as possible of the image inside of the RoI. That is not to say any detail outisde the RoI shall be invisible. It just means that any detail there will not (or much less) have a say in how the stretch is made. For example, if we would have an image of a galaxy, cloned it, put the two image side by side to create a new image, and then specified the RoI perfectly over just one of the cloned galaxies, the other one, outside the RoI would be stretched precisely the same way (as it happens to have exactly the same detail). Whatever detail lies outside the RoI, is simply forced to conform to the stretch that was designed for the RoI.
It is important to note that AutoDev will never clip your blackpoints outside the RoI, unless the 'Outside RoI Influence' parameter is explicitly set to 0% (though it is still not guaranteed to clip even at that setting). Detail outside the RoI may appear very dark (and approach 0/black), but will never be clipped.
Bringing up the 'Outside RoI Influence' parameter will let AutoDev allocate the specified amount of dynamic range to the area outside the RoI as well, at the expens of some dynamic range inside the RoI. If 'Outside RoI Influence' set 100%, then precisely 50% of the dynamic range will be used to show detail inside the RoI and 50% of the dynamic range will be used to show detail outside the RoI. Note that, visually, this behavior is area-size dependent; if the RoI is only a tiny area, the area outside the RoI will have to make do with just 50% of the dynamic range to describe detail for a much larger area (e.g. it has to divide the dynamic range over many more pixels), while the smaller RoI area has much fewer pixels and can therefore allocate each pixel more dynamic range if needed, in turn showing much more detail.
Non-linearly stretching an image's RGB components causes its hue and saturation to be similarly stretched and squashed. This is often observable as "washing out" of coloring in the highlights.
Traditionally, image processing software for astrophotography has struggled with this, resorting to kludges like "special" stretching functions (e.g. ArcSinH) that somewhat minimize the problem, or even procedures that make desaturated highlights adopt the colors of neighboring, non-desaturated pixels.
While other software continues to struggle with color retention, StarTools Tracking feature allows the Color module to go back in time and completely reconstruct the RGB ratios as recorded, regardless of how the image was stretched.
This is one of the major reasons why running the Color module is preferably run as one of the last steps in your processing flow; it is able to completely negate the effect of any stretching - whether global or local - may have had on the hue and saturation of the image.
Because of this, AutoDev's performance is not stymied like some other stretching solutions (e.g. ArcSinH) by a need to preserve coloring. The two aspects - color and luminance - of your image are neatly separated thanks to StarTools' signal evolution Tracking engine.
In the case of a 'Lunar/Planetary' image, reconstructed highlights are allocated additional dynamic range, as to not make them overexpose.
The 'WebVR' button in the module exports your image as a standalone HTML file.
A video is also available that shows a simple, short processing workflow of a real-world, imperfect dataset.
Knowing which features probably represent real DSO detail, the Fractal Flux is an effective de-noiser, sharpener (even for noisy images) and detail augmenter.
'Grain Limit Detail' and 'Grain Limit Color' set the largest visible noise grain size, for detail (luminance) and color respectively, that Denoise 2 should target.
You can convert everything you see to a format you find convenient. Give it a try!