{"id":26189569,"url":"https://github.com/biomedical-imaging-group/psf_generator","last_synced_at":"2025-08-08T07:10:44.111Z","repository":{"id":275955083,"uuid":"747346589","full_name":"Biomedical-Imaging-Group/psf_generator","owner":"Biomedical-Imaging-Group","description":"PSF Generator: a PyTorch-based library to simulate point spread functions for 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PSF-Generator\n\n[![MIT License](https://img.shields.io/github/license/Biomedical-Imaging-Group/psf_generator)](https://github.com/Biomedical-Imaging-Group/psf_generator/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/psf-generator.svg?color=green)](https://pypi.org/project/psf-generator)\n[![Python Version](https://img.shields.io/pypi/pyversions/psf-generator.svg?color=green)](https://python.org)\n\n***\nWelcome to the psf-generator library!\n\nThis library contains a high-performance PyTorch implementation of precise physical models to compute the point spread function (PSF) of optical microscopes. \nThe PSF characterizes the response of an imaging system to a point source and is crucial for tasks such as deconvolution, correction of aberrations, and characterization of the system.\n\nWe classify these models in two types—scalar or vectorial—and in both cases the PSF integral can be computed in Cartesian or spherical coordinate systems. \nThis results in the following four _propagators_\n\n| Name of propagator             |         Other names         |\n|--------------------------------|:---------------------------:|\n| `ScalarCartesianPropagator`    | simple/scalar Fourier model |\n| `ScalarSphericalPropagator`    |       Kirchhoff model       |\n| `VectorialCartesianPropagator` |   vectorial Fourier model   |\n| `VectorialSphericalPropagator` |     Richards-Wolf model     |\n\nFor details on the theory, please refer to \n[Revisiting PSF models: unifying framework and high-performance implementation](https://arxiv.org/html/2502.03170v1).\n\n# Documentation\nDocumentation can be found here: https://psf-generator.readthedocs.io/\n\n# Installation\n\n## Basic Installation\n\n```\npip install psf-generator\n```\n\nThat's it for the basic installation; you're ready to go!\n\n## Developer Installation\n\nIf you're interested in experimenting with the code base, please clone the repository and install it using the following commands:\n```\ngit clone git@github.com:Biomedical-Imaging-Group/psf_generator.git\ncd psf_generator\npip install -e .\n```\n\n# Demos\n\nJupyter Notebook demos and Python scripts can be found under `demos/`.\n\n# Napari Plugin\n\nYou can find our Napari plugin [here](https://github.com/Biomedical-Imaging-Group/napari-psfgenerator).\n\n# Cite Us\n\n```Liu, Yan, Vasiliki Stergiopoulou, Jonathan Chuah, Michael Unser, Daniel Sage, and Jonathan Dong. Revisiting PSF models: unifying framework and high-performance implementation. arXiv preprint [arXiv:2502.03170](https://arxiv.org/abs/2502.03170), 2025.```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomedical-imaging-group%2Fpsf_generator","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiomedical-imaging-group%2Fpsf_generator","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiomedical-imaging-group%2Fpsf_generator/lists"}