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https://github.com/lanl/scico

Scientific Computational Imaging COde
https://github.com/lanl/scico

admm computational-imaging convex-optimization fista inverse-problems jax optimization plug-and-play-priors proximal-algorithms proximal-operators total-variation

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Scientific Computational Imaging COde

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# Scientific Computational Imaging Code (SCICO)



SCICO is a Python package for solving the inverse problems that arise in

scientific imaging applications. Its primary focus is providing methods

for solving ill-posed inverse problems by using an appropriate prior

model of the reconstruction space. SCICO includes a growing suite of

operators, cost functionals, regularizers, and optimization routines

that may be combined to solve a wide range of problems, and is designed

so that it is easy to add new building blocks. SCICO is built on top of

[JAX](https://github.com/google/jax), which provides features such as

automatic gradient calculation and GPU acceleration.



[Documentation](https://scico.rtfd.io/) is available online. If you use

this software for published work, please cite the corresponding [JOSS

Paper](https://doi.org/10.21105/joss.04722) (see bibtex entry

`balke-2022-scico` in `docs/source/references.bib`).



# Installation



See the [online

documentation](https://scico.rtfd.io/en/latest/install.html) for

installation instructions.



# Usage Examples



Usage examples are available as Python scripts and Jupyter Notebooks.

Example scripts are located in `examples/scripts`. The corresponding

Jupyter Notebooks are provided in the

[scico-data](https://github.com/lanl/scico-data) submodule and symlinked

to `examples/notebooks`. They are also viewable on

[GitHub](https://github.com/lanl/scico-data/tree/main/notebooks) or

[nbviewer](https://nbviewer.jupyter.org/github/lanl/scico-data/tree/main/notebooks/index.ipynb),

or can be run online by

[binder](https://mybinder.org/v2/gh/lanl/scico-data/binder?labpath=notebooks%2Findex.ipynb).



# License



SCICO is distributed as open-source software under a BSD 3-Clause

License (see the `LICENSE` file for details).



LANL open source approval reference C20091.



\(c\) 2020-2024. Triad National Security, LLC. All rights reserved. This

program was produced under U.S. Government contract 89233218CNA000001

for Los Alamos National Laboratory (LANL), which is operated by Triad

National Security, LLC for the U.S. Department of Energy/National

Nuclear Security Administration. All rights in the program are reserved

by Triad National Security, LLC, and the U.S. Department of

Energy/National Nuclear Security Administration. The Government has

granted for itself and others acting on its behalf a nonexclusive,

paid-up, irrevocable worldwide license in this material to reproduce,

prepare derivative works, distribute copies to the public, perform

publicly and display publicly, and to permit others to do so.