Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography

Abstract

The newly developed Core Imaging Library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularised reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g., dynamic and spectral. We formalise different optimisation problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state of the art reconstruction methods through case studies and code snapshots.

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Cited by in corpus (7)

• Core Imaging Library -- Part I: a versatile Python framework for tomographic imaging
• CUQIpy: I. Computational uncertainty quantification for inverse problems in Python
• Crystalline phase discriminating neutron tomography using advanced reconstruction methods
• Resolving Full-Wave Through-Wall Transmission Effects in Multi-Static Synthetic Aperture Radar
• Dynamic laboratory X-ray phase-contrast microtomography with structure-based prior regularisation
• Regularization with optimal space-time priors
• Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction