Linear operators are at the core of many of the most basic algorithms for signal and image processing. Matlab’s high-level, matrix-based language allows us to naturally express many of the underlying matrix operations (e.g., computation of matrix-vector products and manipulation of matrices) and is thus a powerful platform on which to develop concrete implementations of these algorithms. Many of the most useful operators, however, do not lend themselves to the explicit matrix representations that Matlab provides. The aim of the Spot Toolbox is to bring the expressiveness of Matlab’s built-in matrix notation to problems for which explicit matrices are not practical.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Bubba, Tatiana A.; Heikkilä, Tommi; Help, Hanna; Huotari, Simo; Salmon, Yann; Siltanen, Samuli: Sparse dynamic tomography: a shearlet-based approach for iodine perfusion in plant stems (2020)
- Dong, Yiqiu; Hansen, Per Christian; Hochstenbach, Michiel E.; Brogaard Riis, Nicolai André: Fixing nonconvergence of algebraic iterative reconstruction with an unmatched backprojector (2019)
- Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
- Bleichrodt, Folkert; van Leeuwen, Tristan; Palenstijn, Willem Jan; van Aarle, Wim; Sijbers, Jan; Batenburg, K. Joost: Easy implementation of advanced tomography algorithms using the ASTRA toolbox with spot operators (2016)
- Da Silva, Curt; Herrmann, Felix J.: Optimization on the hierarchical Tucker manifold - applications to tensor completion (2015)
- Orban, Dominique: Limited-memory LDL(^\top) factorization of symmetric quasi-definite matrices with application to constrained optimization (2015)
- van den Berg, Ewout; Friedlander, Michael P.: Sparse optimization with least-squares constraints (2011)
- van den Berg, Ewout; Friedlander, Michael P.: Probing the Pareto frontier for basis pursuit solutions (2008)