Why does image deconvolution still work with image without sharp edges? Take this image for example:
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$\begingroup$ There are a number of deconvoltion methods, some where the prior point spread function is known, some where it is not. Which one are you referring to? If you know the PSF you do not need to know edge locations. $\endgroup$– Tarin ZiyaeeCommented Dec 15, 2013 at 0:07
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$\begingroup$ @user4619 How does it intuitively make sense that the information isn't simply lost, so deblurring would be imposible. $\endgroup$– MarkaCommented Dec 15, 2013 at 0:43
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$\begingroup$ Because if you know the point-spread-function, (the function that caused the blurring), and you know the final result, you have information you need to remove the PSF's effects. This is the intuition behind it. $\endgroup$– Tarin ZiyaeeCommented Dec 15, 2013 at 16:48
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$\begingroup$ @user4619 But it looks so fake, because it looks like all usefull info is gone. $\endgroup$– MarkaCommented Dec 17, 2013 at 0:52
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$\begingroup$ The goal of deblurring by deconvolution is precisely to restore the edges !? $\endgroup$– user7657Commented Oct 25, 2017 at 14:44
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1 Answer
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Think of it in the frequency domain.
Some of the data is highly damped.
Namely energy in High Frequency is damped with low values.
Yet, unless it is zero identically "There is hope...".
Basically, this is the idea behind deconvolution algorithms.
Restore the energy in frequencies before it was damped.
Practically, we are limited in DR.
Namely if it is damped really hard, even if it not zero, in order to restore it one would amplify it significantly which will also lead to noise.
Regarding the edges, well, assuming we have the right way to recover the enrgies edges will be restored.