Skip Navigation


The Computer Journal Advance Access originally published online on July 29, 2007
The Computer Journal 2009 52(1):64-79; doi:10.1093/comjnl/bxm055
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
52/1/64    most recent
bxm055v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Fadili, M.J.
Right arrow Articles by Murtagh, F.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Inpainting and Zooming Using Sparse Representations

M.J. Fadili1, J.-L. Starck2 and F. Murtagh3,*

1 GREYC CNRS UMR 6072, Image Processing Group, ENSICAEN 14050, Caen Cedex, France
2 CEA-Saclay, DAPNIA/SEDI-SAP, Service d'Astrophysique, F-91191 Gif sur Yvette, France
3 Department of Computer Science, Royal Holloway, University of London, Egham TW20 0EX, UK

* Corresponding author: fionn{at}cs.rhul.ac.uk

Received 30 June 2006; revised 15 February 2007

Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectation maximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, the inpainting/interpolation can be viewed as an estimation problem with missing data. Toward this goal, we propose the idea of using the EM mechanism in a Bayesian framework, where a sparsity promoting prior penalty is imposed on the reconstructed coefficients. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered/interpolated based on sparse representations. We first introduce an easy and efficient sparse-representation-based iterative algorithm for image inpainting. Additionally, we derive its theoretical convergence properties. Compared to its competitors, this algorithm allows a high degree of flexibility to recover different structural components in the image (piecewise smooth, curvilinear, texture, etc.). We also suggest some guidelines to automatically tune the regularization parameter.

Key Words: EM algorithm • sparse representations • inpainting • interpolation • penalized likelihood


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.