Dynamic structure super-resolution

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Storkey, Amos J ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

TREES (MATHEMATICS);

DYNAMIC STRUCTURE; HIGH RESOLUTION DATA; HIGHER RESOLUTION IMAGES; LOW RESOLUTION IMAGES; MEAN FIELD APPROACH; SMOOTHING METHODS; TREE-LIKE ARCHITECTURE; UNSUPERVISED METHOD;

OPTICAL RESOLVING POWER;

EID: 84898941639     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

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2. S. Baker and T. Kanade. Limits on super-resolution and how to break them. In Proceedings of CVPR 00, pages 372-379, 2000.
3. P. Cheeseman, B. Kanefsky, R. Kraft, and J. Stutz. Super-resolved surface reconstruction from multiple images. Technical Report FIA-94-12, NASA Ames, 1994.
4. M. Elad and A. Feuer. Super-resolution reconstruction of image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9): 817-834, 1999.
5. W. T. Freeman, T. R. Jones, and E. C. Pasztor. Markov networks for super-resolution. Technical Report TR-2000-08, MERL, 2000.
6. W. T. Freeman, T. R. Jones, and E. C. Pasztor. Example-based super-resolution. IEEE Computer Graphics and Applications, 2002.
7. R. R. Schultz and R. L. Stevenson. A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing, 3: 233-242, 1994.
8. A. J. Storkey. Dynamic trees: A structured variational method giving efficient propagation rules. In C. Boutilier and M. Goldszmidt, editors, Uncertainty in Artificial Intelligence, pages 566-573. Morgan Kauffmann, 2000.
9. C. K. I. Williams and N. J. Adams. DTs: Dynamic trees. In M. J. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11. MIT Press, 1999.