Chances are you have used a noise reduction routine at some stage. In astrophotography, the problem with most noise reduction routines, is that they have no idea how much worse the noise grain has become in the darker parts. They have no idea how you stretched and processed your image earlier. And they certainly have no idea how you squashed and stretched the noise component locally with wavelet sharpening or local contrast optimisation.
The separation of image processing into dumb filters and objects, is one of the biggest problems for signal fidelity in astrophotographical image processing software today.
In short, the big problem, is that separate image processing routines and filters have no idea what came before, nor what will come after when you invoke them. All pixels are treated the same, regardless of their history. Current image processing routines and filters are still as 'dumb' as they were in the early 90s. It's still "input, output, next".
Without knowing how signal and its noise component evolved to become your final image, trying to, for example, squash noise accurately is impossible. What's too much in one area, is too little in another, all because of the way prior filters have modified the noise component beforehand.
The separation of image processing into dumb filters and objects, is one of the biggest problems for signal fidelity in astrophotographical image processing software today. It is the sole reason for poorer final images, with steeper learning curves than are necessary. Without addressing this fundamental problem, "having more control with more filters and tools" is an illusion. The IKEA effect aside, long workflows with endless tweaking do not make for better images.
But what if every tool, every filter, every algorithm could work backwards from the finished image, and trace signal evolution, per-pixel, all the way back to the source signal? That's Tracking.
Upon opening an image, the Tracking dialog will open, asking you about the characteristics of the data.
A video is also available that shows a simple, short processing workflow of a real-world, imperfect dataset.
Results are free to publish, as long as they are credited "Image acquisition by Jim Misti".
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