The whole question of lens sharpness...

Started Jun 12, 2013 | Discussions thread
olliess
Contributing MemberPosts: 879
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Re: The whole question of lens sharpness...
In reply to Alphoid, Jun 21, 2013

Alphoid wrote:

olliess wrote:

but it is difficult to see how they would apply.

Compare the heat/diffusion kernel to the (model) blur function of a lens.

I don't think those laws say what you think they say. You're using (or occasionally inventing) big words without understanding them.

Look at how a 2-d heat kernel describes the spreading (by heat- or Fickian diffusion) of a point source. Now compare this to the blur kernel (PSF) of a lens.

If you're still not understanding how these are related, then you probably shouldn't go around accusing people of inventing big words.

There is no such thing as a 'diffusion-type process.' The blur of a lens is simply a convolution with the PSF of the lens. If the PSF is exactly known, it can be exactly inverted with a deconvolution.

First off, the problems of finite bandwidth, limited image extent, and noise will limit your ability to invert. And even you assume the PSF of the lens is known perfectly as a function of distance to the subject, it seems a stretch to assume that you know the distance to every point in the unblurred image.

The only bandwidth limit is the Nyquist frequency of your sensor. Limited image extent doesn't matter.

Convolving an image with a PSF means, at least in theory:

1) in the frequency domain, some of the spectrum gets spread beyond the Nyquist limit

2) in the spatial domain, some of the image gets spread beyond the edge of the frame

You're trying to improve lens sharpness, not increase DOF.

What you originally said was:

In theory, given a perfect model of the lens, I can completely undo any blur caused by the lens

So are you now saying you are just trying to "unblur" in the plane of focus, and just accept any side effect for all other distances? (For example, if the PSF of the lens has a different shape at other focal distances, you are now inverting for the wrong PSF everywhere else in the image.)

Noise gets amplified -- I mentioned that -- but it doesn't limit your ability to invert. It's a linear process.

Once you have noise in the problem then it is no longer a linear problem, right?

That's a far cry from a theoretically-guaranteed inversion.

It's exactly what I wrote in my original post.

See above for what you wrote in your original post:

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