https://github.com/matsunagalab/colabbtr
End-to-end differentiable blind tip reconstruction on Colab implemented with PyTorch
https://github.com/matsunagalab/colabbtr
afm atomic-force-microscopy blind-tip-reconstruction differentiable-programming
Last synced: 8 months ago
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End-to-end differentiable blind tip reconstruction on Colab implemented with PyTorch
- Host: GitHub
- URL: https://github.com/matsunagalab/colabbtr
- Owner: matsunagalab
- License: mit
- Created: 2024-01-17T08:29:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-15T06:23:09.000Z (over 1 year ago)
- Last Synced: 2024-11-15T07:23:46.938Z (over 1 year ago)
- Topics: afm, atomic-force-microscopy, blind-tip-reconstruction, differentiable-programming
- Language: Jupyter Notebook
- Homepage:
- Size: 15.8 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ColabBTR
End-to-end differentiable blind tip reconstruction on Colab implemented by PyTorch
## Colab notebook
Easy to use notebook for using the end-to-end differentiable blind tip reconstruction.
[](https://colab.research.google.com/github/matsunagalab/ColabBTR/blob/main/ColabBTR.ipynb)
## Module installation
Requires `PyTorch` and `tqdm`.
If you need the module, install it directly from GitHub:
```
pip install git+https://github.com/matsunagalab/ColabBTR
```
Example pseudo-AFM file is given in `data/`. Please try it if you do not have any AFM data for analysis.
## Citation information
```
Y. Matsunaga, S. Fuchigami, T. Ogane, and S. Takada.
End-to-end differentiable blind tip reconstruction for noisy atomic force microscopy images.
Sci. Rep. 13, 129 (2023).
https://doi.org/10.1038/s41598-022-27057-2
```
## Contact
If you have any questions or troubles, feel free to create GitHub issues, or send email to us.
Yasuhiro Matsunaga
ymatsunaga@mail.saitama-u.ac.jp