Hullo Peter,
On Fri, 29 Apr 2011 23:22:04 +1000, Peter Suetterlin
<[email protected]> wrote:
Assuming this is indeed correct, the problem then for sure was that your
PSF
was not 'general' enough and needs some smoothing. As a start you should
transform your PSF back to normal space and look at it. For motion
blur, it
should mainly be all zero except a line that tracks the movement during
the
exposure...
OK.
For a little while now I have been working on an alternate approach
which attempts to account for non uniform blurring (Whyte et al),
and will continue down this path until I have it working to the
point that I can judge whether it is an approach for this problem,
or it defeats me.
Looking forward to read about your results!
For your info the reference for this is
<http://www.di.ens.fr/willow/research/deblurring/>.
There is Matlab code, and wanting to test how this approach works,
and not having Matlab, my main quest lately has been to get the code
working
in Octave. So, not a lot of experimentation on the Real Problem, but lots
of work on the 'diversion':-)
If you know/use Octave and have an interest in this problem, it would be
good to have some help on this. Oliver White, one of the authors of the
work, has been very helpful but doesn't know Octave.
I will no doubt come back to where I left off, as the idea of being
able to directly derive a PSF from sharp/blurred pair, seems to be
such a good start to a solution, and try your ideas.
Well, in principle this works quite well, it is what we use to correct
astronomical images for seeing (atmospheric distortions). We have two
sets of
images (a set is typically 100 images) that are taken strictly
simultaneous.
One of them can be corrected using statistical methods (Speckle
imaging), so
we get the 'sharp' image for this set. Then for each of the images in
the set
we can derive the PSF from the 'true' image and the blurred one, then
apply
the PSF to the simultaneous image in the other set. But this also only
works
if we have several PSFs to average, and still needs subtle noise
calibration.
Thanks for your help.
Cheers,
--
Regards,
Terry Duell
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