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And lo On Thu, 06 May 2010 15:19:15 +0100, Invisible <voi### [at] dev null> did
spake thusly:
> Echo, focal blur and motion blur are all instances of convolution. That
> means, hypothetically, that by applying a suitable *deconvolution*, you
> should be able to get back the original signal.
>
> Question: Is it actually feasible to do this in the real world?
>
> If I have an image with camera shake, can you really apply some math to
> it and get back the unblurred image? Or is that still the domain of
> science fiction?
You mean something like Unshake?
http://www.zen147963.zen.co.uk/
--
Phil Cook
--
I once tried to be apathetic, but I just couldn't be bothered
http://flipc.blogspot.com
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scott wrote:
>> Echo, focal blur and motion blur are all instances of convolution.
>
> Are you sure that focal blur is a pure 2D convolution?
No. Only if everything in the frame is at approximately the same focal
distance does it approximate a 2D convolution.
> Ditto for motion blur - it's impossible to add motion blur by simply
> using a convolution.
If the motion is "small" relative to the distance of the objects from
the camera, then the blur should be approximately a 2D convolution.
>> That means, hypothetically, that by applying a suitable
>> *deconvolution*, you should be able to get back the original signal.
>>
>> Question: Is it actually feasible to do this in the real world?
>
> The problem is when you apply a convolution to an image, you get a
> bigger image (the size of the original image + the size of the
> convolution window). The extra data around the edges is needed in order
> to do the deconvolution. Obviously in a photo you don't get this
> information.
This merely means that you can't deconvolve the edges properly.
A much bigger problem is figuring out what the hell the convolution
kernel might have been, given only the blurred image...
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On Thu, 06 May 2010 16:19:15 +0200, Invisible <voi### [at] devnull> wrote:
> Echo, focal blur and motion blur are all instances of convolution. That
> means, hypothetically, that by applying a suitable *deconvolution*, you
> should be able to get back the original signal.
>
> Question: Is it actually feasible to do this in the real world?
>
> If I have an image with camera shake, can you really apply some math to
> it and get back the unblurred image? Or is that still the domain of
> science fiction?
http://www.focusmagic.com/
--
FE
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Invisible wrote:
> Echo, focal blur and motion blur are all instances of convolution. That
> means, hypothetically, that by applying a suitable *deconvolution*, you
> should be able to get back the original signal.
>
> Question: Is it actually feasible to do this in the real world?
>
> If I have an image with camera shake, can you really apply some math to
> it and get back the unblurred image? Or is that still the domain of
> science fiction?
The hardest part in practice is determining the blur kernel. Even if
you can determine it exactly the result still won't be perfect, but it
will be noticeably (potentially significantly) deblurred (at least for
camera shake, defocus works less well).
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Kevin Wampler wrote:
> will be noticeably (potentially significantly) deblurred (at least for
> camera shake, defocus works less well).
I would think the kernel for defocus would be different at each pixel,
depending on the original depth from that pixel through the lens to whatever
you're seeing there, yes? Camera shake is probably easier because all the
pixels are going to be blurred with essentially the same convolution (modulo
camera rotation about the axis of the lens).
--
Darren New, San Diego CA, USA (PST)
Linux: Now bringing the quality and usability of
open source desktop apps to your personal electronics.
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Darren New wrote:
> Kevin Wampler wrote:
>> will be noticeably (potentially significantly) deblurred (at least for
>> camera shake, defocus works less well).
>
> I would think the kernel for defocus would be different at each pixel,
> depending on the original depth from that pixel through the lens to
> whatever you're seeing there, yes?
Indeed, but even if everything was all at the same depth the kernel of a
defocus filter tends to be shaped such that reconstruction is less
accurate due to floating-point resolution issues (although since the
kernel is generally simple it's much easier to implement).
If things are at different depths then as you say it's a harder problem.
Ditto for motion blur when parallax matters or there are independently
moving or non-rigid objects in the scene.
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> No. Only if everything in the frame is at approximately the same focal
> distance does it approximate a 2D convolution.
I doubt that happens very often, usually the camera gets a point in focus
you didn't intend (and the bit you did intend is then out of focus).
> If the motion is "small" relative to the distance of the objects from the
> camera, then the blur should be approximately a 2D convolution.
But is it accurate enough to visibly improve the sharpness of an image?
Judging by all the software I've seen that claims to do this, usually not.
> This merely means that you can't deconvolve the edges properly.
True, more precisely a region half the size of the convolution kernel along
each edge.
> A much bigger problem is figuring out what the hell the convolution kernel
> might have been, given only the blurred image...
Indeed, and it might not be constant for every pixel.
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>> Question: Is it actually feasible to do this in the real world?
>
> http://www.focusmagic.com/
Judging by the demo images... it's feasible, but the results aren't
worth it.
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scott wrote:
>> No. Only if everything in the frame is at approximately the same focal
>> distance does it approximate a 2D convolution.
>
> I doubt that happens very often, usually the camera gets a point in
> focus you didn't intend (and the bit you did intend is then out of focus).
No, as demonstrated, the problem I usually have is that my camera's
optics physically can't focus near enough. That blurry image I posted is
of a flat piece of ground. The focal depth is probably near-identical
everywhere. (Except that I think the ground plane might be tilted
relative to the camera...)
> But is it accurate enough to visibly improve the sharpness of an image?
> Judging by all the software I've seen that claims to do this, usually not.
OK. That's kind of what I was asking. DSP theory says it can be done,
but are the results worth it?
(DSP theory also says that a perfect deconvolution would require
infinity gain at certain frequencies. You just *know* that's not going
to work well in the Real World.)
>> This merely means that you can't deconvolve the edges properly.
>
> True, more precisely a region half the size of the convolution kernel
> along each edge.
For the image I'm thinking of, that wouldn't matter. The bit I want is
the person's head in the center.
>> A much bigger problem is figuring out what the hell the convolution
>> kernel might have been, given only the blurred image...
>
> Indeed, and it might not be constant for every pixel.
I suppose if it changes sufficiently slowly, it might still be possible
to estimate... maybe...
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> (Except that I think the ground plane might be tilted relative to the
> camera...)
So each point will have a different convolution kernel...
Maybe if you thought about the optics of your camera you could derive an
approximate kernel as a function of depth and focal distance, you could then
apply this for each pixel using some trial&error method to get the sharpest
image (or some other method).
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