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let people know about python lls deskew deconv code #1
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Looks great! Didn't know about |
I just updated the notebooks, btw (while you were writing your comment) |
@maweigert |
Very cool @VolkerH ! Iterating with smoothing until a single peak local max is left is a nifty trick, haven't seen that before. Do you think there are advantages to that over setting And itkwidgets looks awesome -- definitely going to give that a try. Some last thoughts on flowdec related stuff that may be helpful:
Thanks for sharing that! |
Hi @eric-czech, thanks again for these comments. To be honest, i didn't even look at the options of
and using the property of the Gaussian that it does not shift the location of existing maxima or create new ones. The iterative smoothing is probably unnecessary costly but I'm only doing this once to calculate the PSF. Thanks for the link to the ExtractPSF fiji script. I see they are doing the reverse deconvolution (Distilling) with the bead size. Something I should probably add. I just saw @imagejan last week, I wish I had seen this earlier than I could have gotten some suggestions from him in person. Tagging him, maybe he has some comments. Much of this code should already exist for single molecule localization, which also has things like multi-emitter fitting etc. I will try your suggestions about speeding up things. |
Makes sense. @bnorthan might be interested in what you guys are trying to do to since I think he wrote the original script and might be familiar with other real-time deconvolution efforts in the open source world. FWIW, I'm getting about 370ms to run 10 iterations on a 512x512x64 image/psf (on a GTX 1080). How low would you have to get that for your use case? I think the biggest room for improvement in speed would be in implementing a non-circulant form of RL that would also allow the PSF to be smaller than the image, which is something I've been thinking about working in anyhow but have been too lazy to do so as of yet (it also helps alleviate some border effects). Anyways I'd be happy to help you try to hit a performance goal if you have a sense of whether or not that's close, or to at least see how far a TensorFlow-based implementation can be pushed (I'm sure there are some other minor inefficiencies in there too since real-time wasn't my original objective). For reference: |
Cool, thanks for these benchmarks. That's much faster than I anticipated. |
No problem @VolkerH , that's much closer to our use case too. |
Closing. Discussions can be continued in other issues. |
Hi,
just wanted to make you aware of this repo. Still very early stages but enough there to bring it out in the open. Still uploading files in the coming days/weeks (travelling at the moment)q
@jni @tlambert03 @maweigert @uschmidt83 @eric-czech
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