SplineOps - Spline Operations#
SplineOps is a Python-based open-source software library aimed at providing efficient signal processing tools using splines. Currently adapting and building on the legacy algorithms developed by the Biomedical Imaging Group at EPFL (Lausanne, Switzerland), SplineOps is in active development and evolving to support modern computational demands.
With a focus on handling large datasets, SplineOps supports both CPU and GPU computations, offering tools for data smoothing, interpolation, and other applications. While still a work in progress, the library aims to integrate with the PyData ecosystem and support a variety of research and engineering needs.
By leveraging modern computing architectures, SplineOps seeks to enhance computational workflows while maintaining the rigor and reliability of the original algorithms.
Different representations of spline functions and their derivatives#
Key Features & Capabilities#
Optimized Performance: Leveraging CPU and GPU architectures to handle large-scale signal data sets effectively.
Precision and Flexibility: High-degree spline interpolations across multiple dimensions.
Scalability and Extensibility: To incorporate new functionalities tailored to specific applications.
General B-Spline formula#
References#
Below are the key foundational papers that SplineOps builds upon, with citations in the same style as your existing references.
[Unser1993a] M. Unser, A. Aldroubi, and M. Eden, “B-Spline Signal Processing Part I: Theory,” IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 821–833, 1993. Available at: https://bigwww.epfl.ch/publications/unser9301.html. Full PDF: https://bigwww.epfl.ch/publications/unser9301.pdf.
[Unser1993b] M. Unser, A. Aldroubi, and M. Eden, “B-Spline Signal Processing Part II: Theory and Applications,” IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 834–848, 1993. Available at: https://bigwww.epfl.ch/publications/unser9302.html. Full PDF: https://bigwww.epfl.ch/publications/unser9302.pdf.
[Thévenaz2000] P. Thévenaz, T. Blu, and M. Unser, “Interpolation Revisited,” IEEE Transactions on Medical Imaging, vol. 19, no. 7, pp. 739–758, 2000. Available at: https://bigwww.epfl.ch/publications/thevenaz0002.html. Full PDF: https://bigwww.epfl.ch/publications/thevenaz0002.pdf.