SCICO
A Python package built on JAX for solving inverse problems in scientific imaging using optimization and prior models.
What is SCICO?
SCICO is a Python package for solving inverse problems in scientific imaging applications. It provides methods for solving ill-posed inverse problems by using appropriate prior models of the reconstruction space, enabling accurate image reconstruction from incomplete or noisy data. The package is built on JAX, offering features like automatic gradient calculation and GPU acceleration.
Researchers, scientists, and engineers working in computational imaging, medical imaging, astronomy, microscopy, or any field requiring reconstruction of images from indirect measurements. It is designed for those who need a flexible, high-performance toolkit for solving complex inverse problems.
Developers choose SCICO for its modular design built on JAX, which combines ease of use with high computational performance. Its extensible suite of operators and optimization routines allows for custom problem-solving while leveraging GPU acceleration and automatic differentiation.
Overview
Scientific Computational Imaging COde
Use Cases
Best For
- Reconstructing medical images from limited or noisy sensor data
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