Dear Aurélien,
Am 10.10.2017 um 10:25 schrieb Aurélien PIERRE:
Following my work from this Summer, I'm glad to propose my first blind
deconvolution algorithm, written in Python, based on papers from
2011-2014 : https://github.com/aurelienpierre/Image-Cases-Studies
TL;DR :
Blind deconvolution is a technique used in astronomy and microscopy to
deblur pictures based on an estimation of the blur "profile" (SPF for
the geeks). This work on motion blur, focus blur, etc. A commercial
photo software get spectacular results with this technique :
http://relaunch.piccureplus.com/ (although it's not really blind).
My code is a proof of concept which runs now (barely optimized) between
50 and 275 s on a 2 Mpx image.
I'm still looking for some help to port it in a Darktable module, since
I'm not a C developper (or at least some doc).
I am still rather skeptic regarding the question if a blind
deconvolution algorithm fits into the darktable workflow. It may be
computationally too demanding. Nevertheless, this is a very fascinating
topic and I will have a look at your code. Thanks for your efforts.
Heiko
--
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