4.4 Article

Compressed sensing electron tomography

Journal

ULTRAMICROSCOPY
Volume 131, Issue -, Pages 70-91

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ultramic.2013.03.019

Keywords

Electron tomography; Compressed sensing; Compressive sampling; 3D image reconstruction; Sparsity

Categories

Funding

  1. European Union [312483-ESTEEM2]
  2. European Research Council under European Union [291522-3DIMAGE]
  3. EPSRC [EP/K008218/1, EP/KO39318/1]
  4. Microsoft Research Connections
  5. EPSRC [EP/K008218/1] Funding Source: UKRI
  6. Engineering and Physical Sciences Research Council [EP/K008218/1] Funding Source: researchfish

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The recent mathematical concept of compressed sensing (CS) asserts that a small number of well-chosen measurements can suffice to reconstruct signals that are amenable to sparse or compressible representation. In addition to powerful theoretical results, the principles of CS are being exploited increasingly across a range of experiments to yield substantial performance gains relative to conventional approaches. In this work we describe the application of CS to electron tomography (ET) reconstruction and demonstrate the efficacy of CS-ET with several example studies. Artefacts present in conventional ET reconstructions such as streaking, blurring of object boundaries and elongation are markedly reduced, and robust reconstruction is shown to be possible from far fewer projections than are normally used. The CS-ET approach enables more reliable quantitative analysis of the reconstructions as well as novel 3D studies from extremely limited data. (C) 2013 Elsevier BY. All rights reserved.

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