Single image superresolution based on support vector regression

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 2006, Pages

  Ni, Karl S ^a   Kumar, Sanjeev ^a   Vasconcelos, Nuno ^a   Nguyen, Truong Q ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SUPERRESOLUTION; MEAN-SQUARED ERRORS; SUPERRESOLUTION PROBLEM; VECTOR REGRESSION;

DISCRETE COSINE TRANSFORMS; ERROR ANALYSIS; OPTIMIZATION; REGRESSION ANALYSIS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

IMAGE RESOLUTION;

EID: 33947649525     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. W. Freeman, T. Jones, and E. Pasztor, "Example based super-resolution," IEEE Computer Graphics and Application, March/April 2002.
2. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001, Software available at http:
3. //www.csie.ntu.edu.tw/~cjlin/libsvm.
4. V. Vapnik, Statistical Learning Theory, John Wiley & Sons Inc., 1998.
5. C. A. Micchelli and M. Pontil, "On learning vector-valued functions," Neural Computation, vol. 17, 2005.
6. K. R. Rao and P. Yip, Discrete Cosine Transforms: Algorithms, Advantages, Applications, Academic Press, Inc., 1990.
7. M. O. Franz and Bernhard Scholkopf, "Implicit wiener series for higher-order image analysis," in Advances in Neural Information Processing Systems, 2004.
8. Gert R. G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, 2004.
9. C. S. Ong and A. J. Smola, "Machine learning using hyperkernels," in International Conference on Machine Learning, 2003.