title: Computational Inversion of Electron Tomography Images UsingL2-Gradient Flows.
abstract: In this paper, we present a stable, reliable and robust method for reconstructing a three dimensional density function from a set of two dimensional electric tomographic images. By minimizing an energy functional consisting of a fidelity term and a regularization term, anL2-gradient flow is derived. The flow is integrated by a finite element method in the spatial direction and an explicit Euler scheme in temporal direction. The experimental results show that the proposed method is efficient and effective.
authors.affiliation: State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Guoliang
authors.initials: G
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Computer Sciences and Institute of Computational Engineering & Sciences, University of Texas at Austin, Austin TX 78712.
authors.forename: Ajay
authors.initials: A
authors.lastname: Gopinath
authors.affiliation: Department of Computer Sciences and Institute of Computational Engineering & Sciences, University of Texas at Austin, Austin TX 78712.
authors.forename: Chandrajit
authors.initials: C
authors.lastname: Bajaj
keywords: Computational Inversion; Electric Tomography; Reconstruction