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DeconvOptim.jl

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A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.

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# DeconvOptim.jl

<br>
<a name="logo"/>
<div align="left">
<a href="https://roflmaostc.github.io/DeconvOptim.jl/stable/" target="_blank">
<img src="docs/src/assets/logo.svg" alt="DeconvOptim Logo" width="150"></img>
</a>
</div>
<br>
A package for microscopy image based deconvolution via Optim.jl. This package works with N dimensional <a href="https://github.com/RainerHeintzmann/PointSpreadFunctions.jl">Point Spread Functions</a> 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. 
<br>
  
Deconvolution of a dataset with size 512x256x128 took [2.2 seconds](https://proceedings.juliacon.org/papers/10.21105/jcon.00099) on a RTX 3060 GPU!

## Installation
Type `]`in the REPL to get to the package manager:
```julia
julia> ] add DeconvOptim
```

## Documentation
The documentation of the latest release is [here](https://roflmaostc.github.io/DeconvOptim.jl/stable).
The documentation of current master is [here](https://roflmaostc.github.io/DeconvOptim.jl/dev/).
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("resolution_test_512"))

# generate simple Point Spread Function of aperture radius 30
psf = Float32.(generate_psf(size(img), 30))

# create a blurred, noisy version of that image
img_b = conv(img, psf)
img_n = poisson(img_b, 300)

# deconvolve 2D with default options
@time res, o = deconvolution(img_n, psf)

# deconvolve 2D with no regularizer
@time res_no_reg, o = deconvolution(img_n, psf, regularizer=nothing)

# show final results next to original and blurred version
Gray.([img img_n res])
```
![Results Quick Example](https://raw.githubusercontent.com/roflmaostc/DeconvOptim.jl/master/docs/src/assets/quick_example_results.png)

## Examples
Have a quick look into the [examples folder](examples).
We demonstrate the effect of different regularizers. There is also a [CUDA example](examples/cuda_2D.ipynb). 
Using regularizers together with a CUDA GPU is faster but unfortunately only a factor of ~5-10.
For [3D](examples/cuda_3D.ipynb) 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 `CuArray`s 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](https://proceedings.juliacon.org/papers/10.21105/jcon.00099).
```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](https://nanoimaging.de/) for the great support and discussions during development.
Furthermore without [Tullio.jl](https://github.com/mcabbott/Tullio.jl) and [@mcabbott](https://github.com/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](https://github.com/computational-imaging/ThreeDeconv.jl): works great, CPU performance is much slower, GPU performance is slower
* [Deconvolution.jl](https://github.com/JuliaDSP/Deconvolution.jl): rather simple package with Wiener and Lucy Richardson deconvolution.
* [PointSpreadFunctions.jl](https://github.com/RainerHeintzmann/PointSpreadFunctions.jl): generates point spread functions for microscopy applications

[docs-dev-img]: https://img.shields.io/badge/docs-dev-orange.svg 
[docs-dev-url]: https://roflmaostc.github.io/DeconvOptim.jl/dev/

[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg 
[docs-stable-url]: https://roflmaostc.github.io/DeconvOptim.jl/stable/

[codecov-img]: https://codecov.io/gh/roflmaostc/DeconvOptim.jl/branch/master/graph/badge.svg
[codecov-url]: https://codecov.io/gh/roflmaostc/DeconvOptim.jl

[CI-img]: https://github.com/roflmaostc/DeconvOptim.jl/workflows/CI/badge.svg
[CI-url]: https://github.com/roflmaostc/DeconvOptim.jl/actions?query=workflow%3ACI

DeconvOptim.jl

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