# Python wrapper for BM4D denoising - from Tampere with love, again
Python wrapper for BM4D for stationary correlated noise (including white noise).
BM4D is an algorithm for attenuation of additive spatially correlated
stationary (aka colored) Gaussian noise for volumetric data.
This package provides a wrapper for the BM4D binaries for Python for the
denoising of volumetric and volumetric multichannel data. For denoising of
images/2-D multichannel data, see also the [bm3d](https://pypi.org/project/bm3d) package.
These newer binaries (v4+) are designed only for dealing with additive Gaussian
white or correlated noise. Special features like embedded handling of Rice noise
and adaptive groupwise variance estimation are supported by the legacy binaries
v3.2 at https://webpages.tuni.fi/foi/GCF-BM3D/ .
This implementation is based on
- Y. Mäkinen, L. Azzari, A. Foi, 2020, "Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance
for Improved Shrinkage and Patch Matching", in IEEE Transactions on Image Processing, vol. 29, pp. 8339-8354.
- M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, 2013, "Nonlocal Transform-Domain Filter for Volumetric Data Denoising
and Reconstruction", in IEEE Transactions on Image Processing, vol. 22, pp. 119-133.
- Y. Mäkinen, S. Marchesini, A. Foi, 2022, "Ring Artifact and Poisson Noise Attenuation via Volumetric Multiscale
Nonlocal Collaborative Filtering of Spatially Correlated Noise", in Journal of Synchrotron Radiation, vol. 29, pp. 829-842.
The package contains the BM4D binaries compiled for:
- Windows (Win11, MinGW-64)
- GNU/Linux (GCC 11.3.0, 64-bit)
- Mac OSX (El Capitan, 64-bit): no longer maintained and will be removed from future releases.
- macOS (Sonoma, 64-bit ARM)
The package is available for non-commercial use only. For details, see LICENSE.
Basic usage:
```python
y_hat = bm4d(z, sigma) # white noise: include noise std
y_hat = bm4d(z, psd) # correlated noise: include noise PSD (size of z)
```
For usage examples, see the examples folder of the full source (bm4d-***.tar.gz) from https://pypi.org/project/bm4d/#files .
Contact: Ymir Mäkinen <ymir.makinen@tuni.fi>
Raw data
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"description": "# Python wrapper for BM4D denoising - from Tampere with love, again\n\nPython wrapper for BM4D for stationary correlated noise (including white noise).\n\nBM4D is an algorithm for attenuation of additive spatially correlated\nstationary (aka colored) Gaussian noise for volumetric data.\nThis package provides a wrapper for the BM4D binaries for Python for the \ndenoising of volumetric and volumetric multichannel data. For denoising of \nimages/2-D multichannel data, see also the [bm3d](https://pypi.org/project/bm3d) package.\n\nThese newer binaries (v4+) are designed only for dealing with additive Gaussian\nwhite or correlated noise. Special features like embedded handling of Rice noise\nand adaptive groupwise variance estimation are supported by the legacy binaries\nv3.2 at https://webpages.tuni.fi/foi/GCF-BM3D/ .\n\nThis implementation is based on\n- Y. M\u00e4kinen, L. Azzari, A. Foi, 2020, \"Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance\n for Improved Shrinkage and Patch Matching\", in IEEE Transactions on Image Processing, vol. 29, pp. 8339-8354.\n- M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, 2013, \"Nonlocal Transform-Domain Filter for Volumetric Data Denoising\n and Reconstruction\", in IEEE Transactions on Image Processing, vol. 22, pp. 119-133.\n- Y. M\u00e4kinen, S. Marchesini, A. Foi, 2022, \"Ring Artifact and Poisson Noise Attenuation via Volumetric Multiscale\n Nonlocal Collaborative Filtering of Spatially Correlated Noise\", in Journal of Synchrotron Radiation, vol. 29, pp. 829-842.\n\nThe package contains the BM4D binaries compiled for:\n- Windows (Win11, MinGW-64)\n- GNU/Linux (GCC 11.3.0, 64-bit)\n- Mac OSX (El Capitan, 64-bit): no longer maintained and will be removed from future releases.\n- macOS (Sonoma, 64-bit ARM)\n\nThe package is available for non-commercial use only. For details, see LICENSE.\n\nBasic usage:\n```python\ny_hat = bm4d(z, sigma) # white noise: include noise std\ny_hat = bm4d(z, psd) # correlated noise: include noise PSD (size of z)\n```\n\nFor usage examples, see the examples folder of the full source (bm4d-***.tar.gz) from https://pypi.org/project/bm4d/#files .\n\n\nContact: Ymir M\u00e4kinen <ymir.makinen@tuni.fi> \n",
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