{"id":28719148,"url":"https://github.com/kbredies/tgv_pycuda","last_synced_at":"2026-04-27T18:32:31.127Z","repository":{"id":296543534,"uuid":"986192133","full_name":"kbredies/tgv_pycuda","owner":"kbredies","description":"Algorithms, examples and tests for denoising, deblurring, zooming, dequantization and compressive imaging with total variation (TV) and second-order total generalized variation (TGV) regularization. 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Python implementation with GPU acceleration using PyCUDA.\n\nThe code reproduces, in particular, the numerical experiments in the associated publication:\n\n\u003e Kristian Bredies. Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty. *Lecture Notes in Computer Science*, 8293:44-77, 2014. doi:[10.1007/978-3-642-54774-4_3](https://doi.org/10.1007/978-3-642-54774-4_3)\n \n## Getting started\n\nOne easy way of getting started is to create a Python virtual environment, install the dependencies and to call a test script. For instance, run in the project folder:\n\n```bash\npython -m venv venv\nsource ./venv/bin/activate\npip install -r requirements.txt\npython test_denoise.py\n```\n\nPlease note that a working CUDA installation is required, in particular, a CUDA-enabled GPU. The test scripts are best run in an interactive environment such as `ipython` or `jupyter-notebook`.\n\n```\ntest_denoise.py\ntest_denoise2.py\ntest_denoise3.py\ntest_deblur.py\ntest_deblur2.py\ntest_zoom.py\ntest_zoom2.py\ntest_dequantize.py\ntest_compressed_sensing.py\n```\n\n## Guided examples and figures\n\nA Jupyter Notebook is available that guides through the examples and reproduces the figures in the above-mentioned publication.\n\n```bash\njupyter-notebook examples.ipynb\n```\n\n## Author\n\n* **[Kristian Bredies](https://imsc.uni-graz.at/bredies/)**, [Department of Mathematics and Scientific Computing](https://mathematik.uni-graz.at/en), [University of Graz](https://www.uni-graz.at/en), kristian.bredies@uni-graz.at\n\n ## Acknowledgements\n \nSupport by the [Austrian Science Fund (FWF)](https://www.fwf.ac.at/en/) under grant [SFB F32](https://dx.doi.org/10.55776/F32) (Mathematical Optimization and Applications in Biomedical Sciences) is gratefully acknowledged.\n\nIf you use this code, please cite the associated publication:\n\n\u003e Kristian Bredies. Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty. *Lecture Notes in Computer Science*, 8293:44-77, 2014. doi:[10.1007/978-3-642-54774-4_3](https://doi.org/10.1007/978-3-642-54774-4_3)\n\n```bibtex\n@inbook{Bredies2014,\n  title = {Recovering Piecewise Smooth Multichannel Images by Minimization of Convex Functionals with Total Generalized Variation Penalty},\n  DOI = {10.1007/978-3-642-54774-4_3},\n  booktitle = {Efficient Algorithms for Global Optimization Methods in Computer Vision},\n  publisher = {Springer Berlin Heidelberg},\n  author = {Bredies, Kristian},\n  year = {2014},\n  pages = {44–77}\n}\n```\n\n## License\n\nThis software, excluding third-party components, is licensed under the Apache License, Version 2.0 — see [LICENSE](LICENSE) for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkbredies%2Ftgv_pycuda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkbredies%2Ftgv_pycuda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkbredies%2Ftgv_pycuda/lists"}