Your signal and its noise component

When you stretch your dataset, you will notice noise grain becoming visible quickly in the darker areas..

If you have ever processed an astro image, you will have had to non-linearly stretch the image at some point, to make the darker parts with faint signal visible. Whether you used levels & curves, digital development, or some other tool, you will have noticed noise grain becoming visible quickly.

You may have also noticed that the noise grain always seems to be worse in the darker areas than the in brighter areas. The reason is simple; when you stretch the image to bring out the darker signal, you are also stretching the noise component of the signal along with it.

And the former is just a simple global stretch. Now consider that every pixel's noise component goes through many other transformations and changes as you process your image. Once you get into the more esoteric and advanced operations such as local contrast enhancements or wavelet sharpening, noise levels get distorted in all sorts of different ways in all sorts of different places.

The result? In your final image, noise is worse in some areas, less in others. A "one-noise-reduction-pass-fits-all" no longer applies. Yet that's all other software packages - even the big names - offer. Why? Because tracking that noise grain evolution across your full workflow is very, very hard to implement.