[vanderZwan]

If you have multiple different points where you measure, which all have this overlapping signal problems but at different strengths, couldn't you hypothetically build up a model that "solves" these different weights and untangles the signals?

[dr_zoidberg]

Yes, but... At the end, you're still reconstructing pieces of information from something that was almost destroyed. Picture it this way: there are amazing deconvolution algorithms that can "undo" all sortf of noise and lack of focuse -- but the end result, however good to the original "bad" data isn't nearly as good as a well taken image to begin with.

Disclaimer: I work in image processing, so the example may be a bit obvious to me.

[vanderZwan]

Isn't what I described more like reconstructing a picture from many copies that were each destroyed in a unique fashion?

[dr_zoidberg]

Yes, that'd be a better analogy. My point was that, even if you had the best reconstruction in the world, having to reconstruct from a degraded source is worse than working from a good source to begin with.

[close04]

From a practical perspective they don’t have to be perfect or as food as the original, not even close, just good enough. “Good enough” though is also extremely hard to achieve, assuming it’s even possible with this technique.