Easy and flexible imaging with the Core Imaging Library

What is CIL: The Core Imaging Library was developed by the Collaborative Computational Project in Tomographic Imaging (CCPi), a UK academic network, which unites expertise in the field of Computed Tomography (CT). Its aim is to provide the community with software to increase the quality and level of information that can be extracted by CT, with an emphasis in software sustainability, maintainability and distribution.

CIL for Tomography applications: Tomography, which literally means section imaging, is an inverse problem where the goal is to reconstruct an object, based on penetrating ray-path measurements. This imaging technique requires the solution of an inverse problem and is widespread in different domains such as materials science, medical imaging, biology and geophysics. The goal of CIL is to provide a user-friendly interface that covers all the tomography steps, e.g., data loading, pre-processing, reconstruction, post-processing and visualisation. Specifically, we focus on the reconstruction of real size tomography datasets, which require large memory and computation power, using analytic and iterative reconstruction algorithms. It has been tested in different tomographic scanners such as the

• Nikon Metrology X-ray,
• Diamond Light Source (Synchrotron)
• Neutron Imaging & Diffraction at the Science and Technology Facilities Council (STFC) and the
• X-ray imaging spectroscopy at the Henry Moseley X-ray Imaging Facility

covering a wide range of datasets, from simple single-channel, black-and-white data to more complicated, including hyperspectral and dynamic tomography data.

CIL for imaging applications: The modular design of CIL allows the user to formalise other imaging problems with a direct translation from complicated mathematical expressions to Python code. One can build a simple and fast prototype optimisation framework through a mix & match setting of different existing or user-defined functions and operators and use the CIL algorithms to solve problems such as image denoising, deblurring and inpainting.

This talk will cover the following topics:

• Brief introduction to inverse problems in imaging.
• Motivating examples: denoising, deblurring and inpainting.
• What is Tomography?
• Analytic and iterative tomography reconstruction using CIL. Live jupyter demo.

Takeaways: You will learn how to use the Core Imaging Library for several imaging problems with an emphasis on tomography reconstruction. All the resources for the talk, will be available on Github.