Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Use of learned dictionaries in tomographic reconstruction

[+] Author Affiliations
Vincent Etter, Martin Vetterli

Ecole Polytechnique Fédérale de Lausanne (Switzerland)

Ivana Jovanovic

Ecole Polytechnique Fédérale de Lausanne (Switzerland) and Univ. of Geneva (Switzerland)

Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381C (September 27, 2011); doi:10.1117/12.894776
Text Size: A A A
From Conference Volume 8138

  • Wavelets and Sparsity XIV
  • Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal
  • San Diego, California, USA | August 21, 2011

abstract

We study the use and impact of a dictionary in a tomographic reconstruction setup. First, we build two different dictionaries: one using a set of bases functions (Discrete Cosine Transform), and the other that is learned using patches extracted from training images, similar to the image that we would like to reconstruct. We use K-SVD as the learning algorithm. These dictionaries being local, we convert them to global dictionaries, ready to be applied on whole images, by generating all possible shifts of each atom across the image. During the reconstruction, we minimize the reconstruction error by performing a gradient descent on the image representation in the dictionary space. Our experiments show promising results, allowing to eliminate standard artifacts in the tomographic reconstruction, and to reduce the number of measurements required for the inversion. However, the quality of the results depends on the convergence of the learning process, and on the parameters of the dictionaries (number of atoms, convergence criterion, atom size, etc.). The exact influence of each of these remains to be studied.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Vincent Etter ; Ivana Jovanovic and Martin Vetterli
"Use of learned dictionaries in tomographic reconstruction", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381C (September 27, 2011); doi:10.1117/12.894776; http://dx.doi.org/10.1117/12.894776


Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).

Figures

Tables

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.