While I can't speak to the specific methods used in astro software, I do have a basic understanding of mathematical statistics so I can make some theoretical guesses...
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Originally Posted by gregbradley
Without a decent dark I find the flats just don't work. Strange. Anyone know why that would be?
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The raw image is effectively divided by the flat image on the individual pixel level (i.e. multiplying the dark/vignetted parts so that they are brighter), so without a dark frame you won't be subtracting away the bias and dark signals from your image first. This means that you're increasing the noise of the bias and dark signals - along with the true signal - when attempting to correct with flats.
With a dark frame, the bias and dark signals are subtracted - i.e. effectively become zero - so that only the true signal is being increased.
Quote:
Originally Posted by gregbradley
An ideal computation would detect every artifact yet leave intact bright and dim areas without change. That is the problem the mathematicians are trying to solve with these various combine methods.
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I'm a bit surprised that
compressed sensing methods haven't been used for image reduction yet (to my knowledge, anyway). That stuff is like voodoo magic - it can re-generate images from less than the theoretical minimum sampling than the Nyquist theorem predicts.
(It works because pixels in real world images aren't statistically random like we tend to think - adjacent pixels are actually highly correlated.)