DeconvOptim
A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.
Science Score: 57.0%
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Low similarity (16.8%) to scientific vocabulary
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Repository
A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.
Basic Info
- Host: GitHub
- Owner: roflmaostc
- License: mit
- Language: Julia
- Default Branch: master
- Homepage: https://roflmaostc.github.io/DeconvOptim.jl/stable/
- Size: 21.1 MB
Statistics
- Stars: 65
- Watchers: 4
- Forks: 6
- Open Issues: 10
- Releases: 16
Topics
Metadata Files
README.md
DeconvOptim.jl
A package for microscopy image based deconvolution via Optim.jl. This package works with N dimensional Point Spread Functions and images. The package was created with microscopy in mind but since the code base is quite general it is possible to deconvolve different kernels as well.
Deconvolution of a dataset with size 512x256x128 took 2.2 seconds on a RTX 3060 GPU!
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Installation
Type ]in the REPL to get to the package manager:
julia
julia> ] add DeconvOptim
Documentation
The documentation of the latest release is here. The documentation of current master is here. For a quick introduction you can also watch the presentation at the JuliaCon 2021.
Usage
A quick example is shown below. ```julia using DeconvOptim, TestImages, Colors, ImageIO, Noise, ImageShow
load test image
img = Float32.(testimage("resolutiontest512"))
generate simple Point Spread Function of aperture radius 30
psf = Float32.(generate_psf(size(img), 30))
create a blurred, noisy version of that image
imgb = conv(img, psf) imgn = poisson(img_b, 300)
deconvolve 2D with default options
@time res, o = deconvolution(img_n, psf)
deconvolve 2D with no regularizer
@time resnoreg, o = deconvolution(img_n, psf, regularizer=nothing)
show final results next to original and blurred version
Gray.([img img_n res])
```
Examples
Have a quick look into the examples folder. We demonstrate the effect of different regularizers. There is also a CUDA example. Using regularizers together with a CUDA GPU is faster but unfortunately only a factor of ~5-10. For 3D the speed-up is larger.
CUDA
For CUDA we only provide a Total variation regularizer via TV_cuda. The reason is that Tullio.jl is currently not very fast with CuArrays and especially
the derivative of such functions.
Performance Tips
Regularizers
The regularizers are generated with metaprogramming when TV() (or any other regularizer) is called. To prevent that the code
compile every time again, define the regularizer once and use it multiple times without newly defining it:
julia
reg = TV()
And in the new cell then use:
julia
res, o = deconvolution(img_n, psf, regularizer=reg)
Development
Feel free to file an issue regarding problems, suggestions or improvement ideas for this package! We would be happy to deconvolve real data! File an issue if we can help deconvolving an image/stack. We would be also excited to adapt DeconvOptim.jl to your special needs!
Citation
If you use this paper, please cite it. Thes PDF is linked here.
bibtex
@article{Wechsler2023,
doi = {10.21105/jcon.00099},
url = {https://doi.org/10.21105/jcon.00099},
year = {2023},
publisher = {The Open Journal},
volume = {1},
number = {1},
pages = {99},
author = {Felix Wechsler and Rainer Heintzmann},
title = {DeconvOptim.jl - Signal Deconvolution with Julia},
journal = {Proceedings of the JuliaCon Conferences}
}
Contributions
I would like to thank Rainer Heintzmann for the great support and discussions during development. Furthermore without Tullio.jl and @mcabbott this package wouldn't be as fast as it is. His package and ideas are the basis for the implementations of the regularizers.
Related Packages
- ThreeDeconv: works great, CPU performance is much slower, GPU performance is slower
- Deconvolution.jl: rather simple package with Wiener and Lucy Richardson deconvolution.
- PointSpreadFunctions.jl: generates point spread functions for microscopy applications
Owner
- Name: Felix Wechsler
- Login: roflmaostc
- Kind: user
- Location: EPFL, Lausanne, Switzerland
- Website: https://felixwechsler.science
- Repositories: 96
- Profile: https://github.com/roflmaostc
My passion is combining optics with computer science. Currently PhD @ LAPD @ EPFL
Citation (CITATION.bib)
@article{Wechsler2023,
doi = {10.21105/jcon.00099},
url = {https://doi.org/10.21105/jcon.00099},
year = {2023},
publisher = {The Open Journal},
volume = {1},
number = {1},
pages = {99},
author = {Felix Wechsler and Rainer Heintzmann},
title = {DeconvOptim.jl - Signal Deconvolution with Julia},
journal = {Proceedings of the JuliaCon Conferences}
}
GitHub Events
Total
- Create event: 2
- Commit comment event: 6
- Issues event: 2
- Release event: 1
- Watch event: 4
- Issue comment event: 2
- Push event: 10
- Pull request event: 3
Last Year
- Create event: 2
- Commit comment event: 6
- Issues event: 2
- Release event: 1
- Watch event: 4
- Issue comment event: 2
- Push event: 10
- Pull request event: 3
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| roflmaostc | f****t@m****g | 419 |
| RainerHeintzmann | h****n@g****m | 15 |
| CompatHelper Julia | c****y@j****g | 6 |
| github-actions[bot] | 4****] | 4 |
| Martin Kunz | m****z@e****z | 3 |
| Tim Holy | t****y@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 25
- Total pull requests: 31
- Average time to close issues: 24 days
- Average time to close pull requests: about 1 month
- Total issue authors: 7
- Total pull request authors: 6
- Average comments per issue: 1.44
- Average comments per pull request: 1.23
- Merged pull requests: 19
- Bot issues: 0
- Bot pull requests: 14
Past Year
- Issues: 3
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 2 days
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 0.5
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- roflmaostc (17)
- RainerHeintzmann (3)
- stevengj (1)
- renerichter (1)
- kunzaatko (1)
- JuliaTagBot (1)
Pull Request Authors
- github-actions[bot] (15)
- RainerHeintzmann (11)
- kunzaatko (4)
- jrkager (2)
- timholy (1)
- roflmaostc (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- julia 4 total
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 17
juliahub.com: DeconvOptim
A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.
- Homepage: https://roflmaostc.github.io/DeconvOptim.jl/stable/
- Documentation: https://docs.juliahub.com/General/DeconvOptim/stable/
- License: MIT
-
Latest release: 0.7.4
published 11 months ago
Rankings
Dependencies
- JuliaRegistries/TagBot v1 composite
- actions/cache v1 composite
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- julia-actions/julia-buildpkg v1 composite
- julia-actions/julia-docdeploy releases/v1 composite
- julia-actions/julia-processcoverage v1 composite
- julia-actions/julia-runtest v1 composite
- julia-actions/setup-julia v1 composite