Corpus ID: 119715157

Recovering Fine Details from Under-Resolved Electron Tomography Data using HOTV Regularization

@article{Sanders2016RecoveringFD,
  title={Recovering Fine Details from Under-Resolved Electron Tomography Data using HOTV Regularization},
  author={Toby Sanders and Anne Gelb and Rodrigo B. Platte and Ilke Arslan and Kai Landskron},
  journal={arXiv: Numerical Analysis},
  year={2016}
}
  • Toby Sanders, Anne Gelb, +2 authors Kai Landskron
  • Published 2016
  • Mathematics
  • arXiv: Numerical Analysis
  • Over the last decade or so, reconstruction methods using $\ell_1$ regularization, often categorized as compressed sensing (CS) algorithms, have significantly improved the capabilities of high fidelity imaging in electron tomography. The most popular $\ell_1$ regularization approach within electron tomography has been total variation (TV) regularization. In addition to reducing unwanted noise, TV regularization encourages a piecewise constant solution with sparse boundary regions. In this paper… CONTINUE READING

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    References

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