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 algorithms that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. When solving a problem, these components are combined in a way that makes code for optimization routines look like the pseudocode in scientific papers. 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.

Installation

see on-line documentation.

Usage

see github.

Benchmarks

To be added…