Usage

AutoDev is used for two distinct purposes;

1. To visualise artifacts and problems in your dataset.
2. To stretch the real celestial signal in your dataset
   

Using AutoDev is typically one of the first things a StarTools user does. This is because AutoDev, in the presence of any issues, brings out those issues, just like it would with real detail. Any such issues, for example stacking artifacts, gradients, dust donuts, noise levels, oversampling, etc., can then first be addressed by the relevant modules.

Once the issues have been dealt with to the best of your ability, AutoDev can be used again to stretch your final image to visualise the detail (rather than any artifacts). Do not attempt to use AutoDev for the purpose of bringing out detail if you have not taken care of forementioned artifacts and issues.

Improvements over basic histogram stretching

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.

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 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.

1. Change the 'Ignore Fine Detail <' parameter, so that AutoDev will no longer detect fine detail (such as noise grain).
2. Simply tell it what it should focus on instead by specifying an ROI and not regard the area outside the ROI just a little bit ('Outside ROI influence').

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;

• The 'Detector Gamma' parameter applies - for values other than 1.0 - a non-linear stretch to the image prior to passing it to the detector. E.g. the detector will "see" a darker or brighter image and create a curve that suits this image, rather than the real image. This makes the detector proportionally more (< 1.0) or less (> 1.0) sensitive to detail in the highlights. Conversely it makes the detector less (<1.0) or more (> 1.0) sensitive to detail in the shadows. The effect can be though of as a "smart" gamma correction. Note that tweaking this parameter will, by virtue of its skewing effect, cause the resulting stretch to no longer be optimal.
  
• The 'Shadow Linearity' parameter specifies the amount of linearity that is applied in the shadows, before non-linear stretching takes over. Higher amounts have the effect of allocating more dynamic range to the shadows and background.