title: Compressed sensing electron tomography.
abstract: 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.
authors.affiliation: Department of Materials Science and Metallurgy, University of Cambridge, Pembroke Street, Cambridge CB2 3QZ, UK. rkl26@cam.ac.uk
authors.forename: Rowan
authors.initials: R
authors.lastname: Leary
authors.forename: Zineb
authors.initials: Z
authors.lastname: Saghi
authors.forename: Paul A
authors.initials: PA
authors.lastname: Midgley
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Holland
keywords: 3D image reconstruction; Compressed sensing; Compressive sampling; Electron tomography; Sparsity