Abstract
The main aim of the paper is to introduce a new representation domain advantageously usable also in the field of image processing. In this case the image is represented by polylinear functions on HOSVD basis. The paper gives an overview, how these functions can be reconstructed and how they can be applied in case of image processing. Comparing to other domains, using the proposed domain the image can be expressed by a reduced number of components (polylinear functions) by maintaining its quality. The efficiency of this representation form is demonstrated by performing an image resolution enhancement trough this new domain by maintaining its quality significantly.
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References
De Lathauwer, L., De Moor, B., Vandewalle, J.: A Multilinear Singular Value Decomposition. SIAM Journal on Matrix Analysis and Applications 21(4), 1253–1278 (2000)
Baranyi, P., Szeidl, L., Várlaki, P.: Numerical Reconstruction of the HOSVD-based Canonical Form of Polytopic Dynamic Models. In: Proc. of IEEE 10th Int. Conf. on Intelligent Engineering Systems, London, United Kingdom, June 26-28, 2006, pp. 196–201 (2006)
Szeidl, L., Baranyi, P., Petres, Z., Várlaki, P.: Numerical Reconstruction of the HOSVD-based Canonical Form of Polytopic Dynamic Models. In: 3rdInternational Symposium on Computational Intelligence and Intelligent Informatics, Agadir, Morocco, pp. 111–116 (2007)
Szeidl, L., Rudas, I., Rövid, A., Várlaki, P.: HOSVD-based Method for Surface Data Approximation and Compression. In: 12thInternational Conference on Intelligent Engineering Systems, Miami, Florida, February 25-29, 2008, pp. 197–202 (2008) 978-1-4244-2083-4
Rövid, A., Várlaki, P.: Method for Merging Multiple Exposure Color Image Data. In: 13thInternational Conference on Intelligent Engineering Systems, Barbados, April 16-18, 2009, pp. 27–31 (2009)
Szeidl, L., Várlaki, P.: HOSVD-based Canonical Form for Polytopic Models of Dynamic Systems. Journal of Advanced Computational Intelligence and Intelligent Informatics 13(1), 52–60 (2009)
Horé, A., Deschênes, F., Ziou, D.: A New Super-Resolution Algorithm Based on Areas Pixels and the Sampling Theorem of Papoulis. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 97–109. Springer, Heidelberg (2008)
Gao, R., Song, J.-P., Tai, X.-C.: Image Zooming Algorithm Based on Partial Differential Equations Technique. International Journal of Numerical Analysis and Modeling 6(2), 284–292 (2009)
Nickolaus, M., Yue, L., Do Minh, N.: Image Interpolation Using Mulitscale Geometric Representations. In: Proceedings of the SPIE, vol. 6498, pp. 1–11 (2007)
Su, D., Willis, P.: Image Interpolation by Pixel-Level Data-Dependent Triangulation. Computer Graphics Forum 23(2), 189–201 (2004)
Yuan, S., Abe, M., Taguchi, A., Kawamata, M.: High Accuracy Bicubic Interpolation Using Image Local Features. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A(8), 1611–1615 (2007)
Bockaert, V.: Digital Photography Review - Interpolation (Cited, April 2009)
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Rövid, A., Szeidl, L. (2009). Image Processing Using Polylinear Functions on HOSVD Basis. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Towards Intelligent Engineering and Information Technology. Studies in Computational Intelligence, vol 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03737-5_30
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DOI: https://doi.org/10.1007/978-3-642-03737-5_30
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