# DeBCR
**DeBCR** is a Python-based framework for light microscopy data enhancement, including denoising and deconvolution.
As an enhancement core, **DeBCR** implements a multi-scale sparsity-efficient deep learning model [m-rBCR](https://doi.org/10.1007/978-3-031-73226-3_22).
As a framework, **DeBCR** provides user interfaces such as:
- [`debcr`](https://github.com/DeBCR/DeBCR) - a Python-based API library for scripting, e.g. using [Jupyter Notebook/Lab](https://jupyter.org/)
- [`napari-debcr`](https://github.com/DeBCR/napari-debcr/) - an add-on GUI plugin for [Napari viewer](https://github.com/napari/napari)
### License
This is an open-source project and is licensed under [MIT license](https://github.com/DeBCR/DeBCR/blob/main/LICENSE).
### Contact
For any questions or bug-reports on `debcr` please use dedicated [GitHub Issue Tracker](https://github.com/DeBCR/DeBCR/issues).
## Installation
There are two hardware-based installation options for `debcr`:
- `debcr[tf-gpu]` - for a GPU-based trainig and prediction (**recommended**);
- `debcr[tf-cpu]` - for a CPU-only execution (note: training on CPUs might be quite slow!).
### GPU prerequisites
For a GPU version you need:
- a GPU device with at least 12Gb of VRAM;
- a compatible CUDA Toolkit (recommemded: [CUDA-11.7](https://developer.nvidia.com/cuda-11-7-0-download-archive));
- a compatible cuDNN library (recommemded: v8.4.0 for CUDA-11.x from [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive)).
For more info on GPU dependencies please check our [GPU-advice page](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md).
### Create a package environment (optional)
For a clean isolated installation, we advice using one of Python package environment managers, for example:
- `micromamba`/`mamba` (see [mamba.readthedocs.io](https://mamba.readthedocs.io/))
- `conda-forge` (see [conda-forge.org](https://conda-forge.org/))
Create an environment for `debcr` using
```bash
micromamba env create -n debcr python=3.9 -y
```
and activate it for further installation or usage by
```bash
micromamba activate debcr
```
### Install DeBCR
Install one of the `DeBCR` versions:
- GPU (**recommended**; backend: TensorFlow-GPU-v2.11):
```bash
pip install 'debcr[tf-gpu]'
```
- CPU (*limited*; backend: TensorFlow-CPU-v2.11)
```bash
pip install 'debcr[tf-cpu]'
```
### Test GPU visibility
For a GPU version installation, it is recommended to check if your GPU device is recognised by **TensorFlow** using
```bash
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
which for a single GPU device should produce a similar output as below:
```
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
```
If your GPU device list is empty, please check our [GPU-advice page](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md).
### Install Jupyter
To use `debcr` as a Python library (API) interactively, please also install [Jupyter Notebook/Lab](https://jupyter.org/install), for example
```bash
pip install jupyterlab
```
## Usage
To learn using `debcr` as a python library (API) interactively, follow our notebook tutorials:
| Notebook tutorial | Purpose | Hardware | Inputs |
| :---------------------------------------------------------------- | :------ | :------- | :------- |
| [debcr_predict.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_predict.ipynb) | enhanced prediction | CPU/GPU | pre-processed input data (NPZ/NPY), </br> trained DeBCR model. |
| [debcr_train.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_train.ipynb) | model training | GPU | training/validation data (NPZ/NPY). |
| [debcr_preproc.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_preproc.ipynb) | raw data pre-processing | CPU | raw data (TIF/TIFF, JPG/JPEG, PNG). |
To use these notebooks,
1. activate `debcr` environment, if was inactive, by
```bash
micromamba activate debcr
```
2. start Jupyter session at the notebooks location (download them from the [DeBCR GitHub](https://github.com/DeBCR/DeBCR))
```bash
jupyter-lab
```
### Example data and trained model weights
Based on several previously published datasets (from [CARE](https://edmond.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.FDFZOF), [DeepBacs](https://doi.org/10.5281/zenodo.12626121), and [TA-GAN](https://doi.org/10.5281/zenodo.7908913)), we prepared four example datasets and trained `m-rBCR` model weights to both evaluate our model and serve as the example data/weights for notebook tutorials.
The datasets are distributed as NumPy (.npz) arrays in three essential sets (train, validation and test), available along with the trained model weights on Zenodo: [10.5281/zenodo.12626121](https://zenodo.org/doi/10.5281/zenodo.12626121).
## About model
The core **DeBCR** enhancement model **m-rBCR** approximates imaging process inversion with deep convolutional neural network (DCNN), based on compact BCR-representation ([Beylkin G. et al., *Comm. Pure Appl. Math*, 1991](https://onlinelibrary.wiley.com/doi/10.1002/cpa.3160440202)) for convolutions and its DCNN implementation as proposed in BCR-Net ([Fan Y. et al., *J. Comput. Phys.*, 2019](https://www.sciencedirect.com/science/article/pii/S0021999119300762)):
In contrast to the traditional single-stage residual BCR learning process, the core DeBCR model integrates feature maps from multiple resolution levels:
The example of the **DeBCR** performance on the low/high exposure confocal data of *Tribolium castaneum* sample from the **CARE** work ([Weigert et al., *Nat. Methods*, 2018](https://www.nature.com/articles/s41592-018-0216-7)) is shown below:
For more details on the multi-stage residual BCR (m-rBCR) architechture implemented within DeBCR framework see:
Li, R., Kudryashev, M., Yakimovich, A. Solving the Inverse Problem of Microscopy Deconvolution with a Residual Beylkin-Coifman-Rokhlin Neural Network. *ECCV 2024*, *Lecture Notes in Computer Science*, vol 15133. Springer, Cham. https://doi.org/10.1007/978-3-031-73226-3_22
<!--
For more details on implementaion and benchmarks please see our recent preprint:
Li R., Yushkevich A., Chu X., Kudryashev M., Yakimovich A. Denoising, Deblurring, and optical Deconvolution for cryo-ET and light microscopy with a physics-informed deep neural network DeBCR. *bioRxiv*, 2024.
-->
Raw data
{
"_id": null,
"home_page": "https://github.com/DeBCR/DeBCR",
"name": "debcr",
"maintainer": "Artsemi Yushkevich",
"docs_url": null,
"requires_python": "<3.12,>=3.9",
"maintainer_email": null,
"keywords": "image processing, image restoration, image enhancement, deep learning, BCR, BCR-Net, m-rBCR, denoising, deblurring, deconvolution, confocal microscopy, widefield microscopy, fluorescence microscopy, light microscopy",
"author": "Artsemi Yushkevich",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/f5/6e/9adb86f90cc8eef04c9b9912bc0faf323f1ca031bd77989e177ea1a680a4/debcr-0.1.0.tar.gz",
"platform": null,
"description": "# DeBCR\n\n**DeBCR** is a Python-based framework for light microscopy data enhancement, including denoising and deconvolution.\n\nAs an enhancement core, **DeBCR** implements a multi-scale sparsity-efficient deep learning model [m-rBCR](https://doi.org/10.1007/978-3-031-73226-3_22).\n\nAs a framework, **DeBCR** provides user interfaces such as:\n- [`debcr`](https://github.com/DeBCR/DeBCR) - a Python-based API library for scripting, e.g. using [Jupyter Notebook/Lab](https://jupyter.org/)\n- [`napari-debcr`](https://github.com/DeBCR/napari-debcr/) - an add-on GUI plugin for [Napari viewer](https://github.com/napari/napari)\n\n### License\nThis is an open-source project and is licensed under [MIT license](https://github.com/DeBCR/DeBCR/blob/main/LICENSE).\n\n### Contact\nFor any questions or bug-reports on `debcr` please use dedicated [GitHub Issue Tracker](https://github.com/DeBCR/DeBCR/issues).\n\n## Installation\n\nThere are two hardware-based installation options for `debcr`:\n- `debcr[tf-gpu]` - for a GPU-based trainig and prediction (**recommended**);\n- `debcr[tf-cpu]` - for a CPU-only execution (note: training on CPUs might be quite slow!).\n\n### GPU prerequisites\n\nFor a GPU version you need:\n- a GPU device with at least 12Gb of VRAM;\n- a compatible CUDA Toolkit (recommemded: [CUDA-11.7](https://developer.nvidia.com/cuda-11-7-0-download-archive));\n- a compatible cuDNN library (recommemded: v8.4.0 for CUDA-11.x from [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive)).\n\nFor more info on GPU dependencies please check our [GPU-advice page](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md). \n\n### Create a package environment (optional)\n\nFor a clean isolated installation, we advice using one of Python package environment managers, for example:\n- `micromamba`/`mamba` (see [mamba.readthedocs.io](https://mamba.readthedocs.io/))\n- `conda-forge` (see [conda-forge.org](https://conda-forge.org/))\n\nCreate an environment for `debcr` using\n```bash\nmicromamba env create -n debcr python=3.9 -y\n```\nand activate it for further installation or usage by\n```bash\nmicromamba activate debcr\n```\n\n### Install DeBCR\n\nInstall one of the `DeBCR` versions:\n- GPU (**recommended**; backend: TensorFlow-GPU-v2.11):\n ```bash\n pip install 'debcr[tf-gpu]'\n ```\n- CPU (*limited*; backend: TensorFlow-CPU-v2.11)\n ```bash\n pip install 'debcr[tf-cpu]'\n ```\n\n### Test GPU visibility\n\nFor a GPU version installation, it is recommended to check if your GPU device is recognised by **TensorFlow** using\n```bash\npython -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\n```\n\nwhich for a single GPU device should produce a similar output as below:\n```\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n```\n\nIf your GPU device list is empty, please check our [GPU-advice page](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md). \n\n### Install Jupyter\n\nTo use `debcr` as a Python library (API) interactively, please also install [Jupyter Notebook/Lab](https://jupyter.org/install), for example\n```bash\npip install jupyterlab\n```\n\n## Usage\n\nTo learn using `debcr` as a python library (API) interactively, follow our notebook tutorials:\n\n| Notebook tutorial | Purpose | Hardware | Inputs |\n| :---------------------------------------------------------------- | :------ | :------- | :------- | \n| [debcr_predict.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_predict.ipynb) | enhanced prediction | CPU/GPU | pre-processed input data (NPZ/NPY), </br> trained DeBCR model. |\n| [debcr_train.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_train.ipynb) | model training | GPU | training/validation data (NPZ/NPY). |\n| [debcr_preproc.ipynb](https://github.com/DeBCR/DeBCR/blob/main/notebooks/debcr_preproc.ipynb) | raw data pre-processing | CPU | raw data (TIF/TIFF, JPG/JPEG, PNG). |\n\nTo use these notebooks,\n1. activate `debcr` environment, if was inactive, by\n```bash\nmicromamba activate debcr\n```\n2. start Jupyter session at the notebooks location (download them from the [DeBCR GitHub](https://github.com/DeBCR/DeBCR))\n```bash\njupyter-lab\n```\n\n### Example data and trained model weights\n\nBased on several previously published datasets (from [CARE](https://edmond.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.FDFZOF), [DeepBacs](https://doi.org/10.5281/zenodo.12626121), and [TA-GAN](https://doi.org/10.5281/zenodo.7908913)), we prepared four example datasets and trained `m-rBCR` model weights to both evaluate our model and serve as the example data/weights for notebook tutorials.\n\nThe datasets are distributed as NumPy (.npz) arrays in three essential sets (train, validation and test), available along with the trained model weights on Zenodo: [10.5281/zenodo.12626121](https://zenodo.org/doi/10.5281/zenodo.12626121).\n\n## About model\n\nThe core **DeBCR** enhancement model **m-rBCR** approximates imaging process inversion with deep convolutional neural network (DCNN), based on compact BCR-representation ([Beylkin G. et al., *Comm. Pure Appl. Math*, 1991](https://onlinelibrary.wiley.com/doi/10.1002/cpa.3160440202)) for convolutions and its DCNN implementation as proposed in BCR-Net ([Fan Y. et al., *J. Comput. Phys.*, 2019](https://www.sciencedirect.com/science/article/pii/S0021999119300762)):\n\n\n\nIn contrast to the traditional single-stage residual BCR learning process, the core DeBCR model integrates feature maps from multiple resolution levels:\n\n\nThe example of the **DeBCR** performance on the low/high exposure confocal data of *Tribolium castaneum* sample from the **CARE** work ([Weigert et al., *Nat. Methods*, 2018](https://www.nature.com/articles/s41592-018-0216-7)) is shown below:\n\n\nFor more details on the multi-stage residual BCR (m-rBCR) architechture implemented within DeBCR framework see:\n\nLi, R., Kudryashev, M., Yakimovich, A. Solving the Inverse Problem of Microscopy Deconvolution with a Residual Beylkin-Coifman-Rokhlin Neural Network. *ECCV 2024*, *Lecture Notes in Computer Science*, vol 15133. Springer, Cham. https://doi.org/10.1007/978-3-031-73226-3_22\n\n<!--\nFor more details on implementaion and benchmarks please see our recent preprint:\nLi R., Yushkevich A., Chu X., Kudryashev M., Yakimovich A. Denoising, Deblurring, and optical Deconvolution for cryo-ET and light microscopy with a physics-informed deep neural network DeBCR. *bioRxiv*, 2024.\n-->\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Image enhancement framework for light microscopy data using deep learning.",
"version": "0.1.0",
"project_urls": {
"Homepage": "https://github.com/DeBCR/DeBCR"
},
"split_keywords": [
"image processing",
" image restoration",
" image enhancement",
" deep learning",
" bcr",
" bcr-net",
" m-rbcr",
" denoising",
" deblurring",
" deconvolution",
" confocal microscopy",
" widefield microscopy",
" fluorescence microscopy",
" light microscopy"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4d4c991c6b87bb22bbbe5260336b0cde17d2c9af28a7d7bdaf21f40b32fba286",
"md5": "26438959942504c97b59a6863a7638db",
"sha256": "d0dfa507c59ab58b9faa6e8f650429804d8a040ffaa97e83e56772938898ef7f"
},
"downloads": -1,
"filename": "debcr-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "26438959942504c97b59a6863a7638db",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.12,>=3.9",
"size": 23740,
"upload_time": "2025-07-08T12:12:02",
"upload_time_iso_8601": "2025-07-08T12:12:02.161637Z",
"url": "https://files.pythonhosted.org/packages/4d/4c/991c6b87bb22bbbe5260336b0cde17d2c9af28a7d7bdaf21f40b32fba286/debcr-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f56e9adb86f90cc8eef04c9b9912bc0faf323f1ca031bd77989e177ea1a680a4",
"md5": "dc9ac2df992c3db7a96251c85120df1b",
"sha256": "40b588b9a0a9b7e4987b0708bf7319443a791b02879236bd6d7c851c4b9827f4"
},
"downloads": -1,
"filename": "debcr-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "dc9ac2df992c3db7a96251c85120df1b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.9",
"size": 20994,
"upload_time": "2025-07-08T12:12:03",
"upload_time_iso_8601": "2025-07-08T12:12:03.656396Z",
"url": "https://files.pythonhosted.org/packages/f5/6e/9adb86f90cc8eef04c9b9912bc0faf323f1ca031bd77989e177ea1a680a4/debcr-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-08 12:12:03",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "DeBCR",
"github_project": "DeBCR",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "debcr"
}
JSON
