KaaX wrote: 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:
...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.
but the difference is high-freq info is something that we want restored and can be, random sensor noise is something totally different even if it is high frequency too, sure it can maybe mess with fixing up the high-freq detail but that is another matter
Mar 22, 2012 at 04:27 PM
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