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.
Quantization noise (limiting sensor readout to a discrete set of integers) is a form of sensor noise, though usually lower than shot noise and read noise in real-life sensors.
That\'s what I\'ve been harping on. The sensor noise is not a limit.
...
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.
Sensor noise typically has components at all frequencies. The high frequency components are more relevant to sharpening/deconvolution, because they will be magnified more.
I\'ve already shown a working proof-of-principle that recovers \"legitimate\" high frequency noise from a synthetically blurred sample image --- in the sufficient absence of noise (sensor or computational), the deconvolution process works just fine. Sensor noise is \"the limit\" in the sense that the presence of this noise is a primary obstacle to real-life deconvolution. Lower noise images could be sharpened/deconvolved more strongly to recover more \"legitimate\" detail.
One more example, I\'ve doubled my blur radius and boosted the internal working precision of my code to double throughout. More blurred image:
recovered deconvolution:
this level of precision isn\'t quite enough to handle all the extra blur, so the results are inferior to my earlier, less demanding blur examples. Nonetheless, my point is that, with sufficiently beefy computer backing, the deconvolution method can be pushed as far as you want for \"ideal\" cases. The real world limits hit much earlier than even my previous example due to a variety of factors, among which noise (in many forms) is a serious problem.
Mar 22, 2012 at 04:34 PM
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