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- W2947431243 abstract "Ill-posed image recovery requires regularisation to ensure stability. The presented open-source regularisation toolkit consists of state-of-the-art variational algorithms which can be embedded in a plug-and-play fashion into the general framework of proximal splitting methods. The packaged regularisers aim to satisfy various prior expectations of the investigated objects, e.g., their structural characteristics, smooth or non-smooth surface morphology. The flexibility of the toolkit helps with the design of more advanced model-based iterative reconstruction methods for different imaging modalities while operating with simpler building blocks. The toolkit is written for CPU and GPU architectures and wrapped for Python/MATLAB. We demonstrate the functionality of the toolkit in application to Positron Emission Tomography (PET) and X-ray synchrotron computed tomography (CT)." @default.
- W2947431243 created "2019-06-07" @default.
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- W2947431243 date "2019-05-28" @default.
- W2947431243 modified "2023-09-23" @default.
- W2947431243 title "Versatile regularisation toolkit for iterative image reconstruction with proximal splitting algorithms" @default.
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- W2947431243 doi "https://doi.org/10.1117/12.2534289" @default.
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