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https://github.com/weisongzhao/sparse-deconv-py

Official Python implementation of the 'Sparse deconvolution'-v0.3.0
https://github.com/weisongzhao/sparse-deconv-py

fluorescence-microscopy-imaging image-processing image-restoration microscopy python3 super-resolution

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Official Python implementation of the 'Sparse deconvolution'-v0.3.0

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README 

        
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Sparse deconvolution Python v0.3.0




  



Official **Python** implementation of the '**Sparse deconvolution**', and the `CPU (NumPy)` and `GPU (CuPy)` calculation backend will be automatically selected. 



We haven’t tested it thoroughly, and the development is work in progress, so expect rough edges. As a result, feedback, questions, bug reports, and patches are welcome and encouraged!



It is a part of publication. For details, please refer to: "[Weisong Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy, Nature Biotechnology (2021)](https://doi.org/10.1038/s41587-021-01092-2)".



## Instruction



- NOTE: The MATLAB version and detailed information can be found at https://github.com/WeisongZhao/Sparse-SIM.

- NOTE: The GPU acceleration feature using CuPy requires a CUDA-based NVIDIA GPU. It could provide a ~30 times faster reconstruction speed for a `512 × 512 × 5` image stack.

- Clone/download, and run the `demo.py`



```python

from sparse_recon.sparse_deconv import sparse_deconv



im = io.imread('test.tif')

plt.imshow(im,cmap = 'gray')

plt.show()



pixelsize = 65 #(nm)

resolution = 280 #(nm)



img_recon = sparse_deconv(im, resolution / pixelsize)

plt.imshow(img_recon / img_recon.max() * 255,cmap = 'gray')

plt.show()

```



## Tested dependency 



- Python 3.7

- NumPy 1.21.4

- CuPy 9.6.0 (CUDA 11.5)

- PyWavelets 1.1.1



## Version



- v0.3.0 full Sparse deconvolution features

- v0.2.0 iterative deconvolution

- v0.1.0 initialized and started from [dzh929](https://github.com/dzh929/Sparse-SIM-python)



## Related links: 

- MATLAB version of Sparse deconvolution: [MATLAB version](https://github.com/WeisongZhao/Sparse-SIM)

- A light weight MATLAB library for making exsiting images to videos: [img2vid](https://github.com/WeisongZhao/img2vid)

- An adaptive filter to remove isolate hot pixels: [Adaptive filter imagej-plugin](https://github.com/WeisongZhao/AdaptiveMedian.imagej)

- A tool for multi-color 2D or 3D imaging: [Merge channels](https://github.com/WeisongZhao/Palette.ui)

- **Further reading:** [#behind_the_paper](https://bioengineeringcommunity.nature.com/posts/physical-resolution-might-be-meaningless-if-in-the-mathmetical-space) & [blog](https://weisongzhao.github.io/rl_positivity_sim)

- **Some fancy results and comparisons:** [my website](https://weisongzhao.github.io/home/portfolio-4-col.html#Sparse)

- **Preprint:** [Weisong Zhao et al., Extending resolution of structured illumination microscopy with sparse deconvolution, Research Square (2021).](https://doi.org/10.21203/rs.3.rs-279271/v1)

- **Reference:** [Weisong Zhao et al., Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy, Nature Biotechnology (2021).](https://doi.org/10.1038/s41587-021-01092-2)



## Open source [Sparse deconvolution](https://github.com/WeisongZhao/Sparse-deconv-py)



- This software and corresponding methods can only be used for **non-commercial** use, and they are under Open Data Commons Open Database License v1.0.

- Feedback, questions, bug reports and patches are welcome and encouraged!