Sparse Representations for Limited Data Tomography (PREPRINT)

Abstract

In limited data tomography, with applications such as electron microscopy and medical imaging, the scanning views are within an angular range that is often both limited and sparsely sampled. In these situations, standard algorithms produce reconstructions with notorious artifacts. We show in this paper that a sparsity image representation principle, based on learning dictionaries for sparse representations of image patches, leads to significantly improved reconstructions of the unknown density from its limited angle projections. The presentation of the underlying framework is complemented with illustrative results on artificial and real data.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2007
Accession Number
ADA478588

Entities

People

  • Guillermo Sapiro
  • Hstau Y. Liao

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artifacts
  • Boundaries
  • Diagnostic Imaging
  • Dictionaries
  • Electron Microscopy
  • Gray Scale
  • Image Processing
  • Image Reconstruction
  • Iterations
  • Learning
  • Mathematics
  • Minnesota
  • Standards
  • Teeth
  • Tomography
  • Uncertainty

Readers

  • Medical Imaging.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • Microelectronics