mpmendenhall wrote:
And, as I\'ve tried to repeatedly point out, the reason for this limit is the \"noise floor\" from rounding/error in the computations.
Yes. But not only that. You\'re also constrained, for example, by the limited precision of the pixel values which are, after all, integers.
mpmendenhall wrote:
...not limited by noise from sensor...
That\'s what I\'ve been harping on. The sensor noise is not a limit.
Consider it from the math point of view. Convolution and deconvolution operate on a matrix of numbers. They completely don\'t care where these numbers come from or what do they mean. If the image has a lot of high-frequency data, you can\'t tell without looking at the image whether that high-frequency data is fine detail or noise. That\'s basically what sensor noise is, it\'s the high-frequency component. But you can get the same high-frequency component in other, \"legitimate\" ways, too, where it would represent desirable fine detail.
Consider, oh, I don\'t know, say images of fractals. Synthetic images, no \"noise\", right? But there\'s a huge high-frequency element there. From a convolution/deconvolution point of view, what\'s the difference from a photograph full of sensor noise?
Clearly, deconvolution works. But also clearly it\'s a somewhat fragile procedure. Push it beyond it\'s envelope and it messily collapses. It\'s not easy to make it work in photographic reality -- it\'s no accident that only now we\'re starting to get first de-blur applications.
Mar 22, 2012 at 04:14 PM
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