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DOE OSTI · 1898364

Scientific Computational Imaging Code (SCICO)

Balke, Thilo·Davis Rivera, Fernando·Garcia-Cardona, Cristina·Majee, Soumendu·McCann, Michael Thompson·Pfister, Luke·Wohlberg, Brendt Egon
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Abstract

Scientific Computational Imaging Code (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 rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.

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Balke, Thilo, Davis Rivera, Fernando, Garcia-Cardona, Cristina, Majee, Soumendu, McCann, Michael Thompson, Pfister, Luke, Wohlberg, Brendt Egon. 2022-10-28. Scientific Computational Imaging Code (SCICO). https://doi.org/10.21105/joss.04722

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