The testing results achieved by using these interpolation methods are presented for increasing gray-scale image resolution, for image reconstruction and the encoding-decoding the images necessary for their transmission.
📌 3 Maddede Bu Makale
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Amaç & Hipotez:In this paper some new interpolation methods using cellular neural networks (CNN) are proposed.
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Temel Bulgular & Veri:The templates have only linear feedback component with a 3*3 dimension.
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Sonuç & Katkı:The testing results achieved by using these interpolation methods are presented for increasing gray-scale image resolution, for image reconstruction and the encoding-decoding the images necessary for their transmission.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri
Abstract
In this paper some new interpolation methods using cellular neural networks (CNN) are proposed. The templates have only linear feedback component with a 3*3 dimension. The templates were computed by using analytic methods. By implementing these interpolation methods on a “CNN-Universal Chip”, the processing becomes completely parallel and it can be done in real time. The testing results achieved by using these interpolation methods are presented for increasing gray-scale image resolution, for image reconstruction and the encoding-decoding the images necessary for their transmission.
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Bu Makaleye Atıf Yap
GACSADI, A., SZOLGAY, P. (2022). Interpolation of 2D signals using cellular neural network. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 3 - Sayı: 1, 635–640. https://ajindex.com/articles/543909
L.O. Chua, L. Yang, “Cellular Neural Networks: Theory and Applications”, IEEE Trans. on Circuits and Systems, Vol.35, pp. 1257-1290, 1988
T. Roska, L.O. Chua, “The CNN universal machine: an analogic array computer”, IEEE Trans. on Circuits and Systems, Vol.40, pp. 163-173, 1993
G. Linan, S. Espejo, R. Dominguez-Castro, E. Roca, A. Rodriguez-Vazquez, “CNNUM3: A mixed-signal 64x64 CNN Universal Chip”, Proc. of Int. Conf. on Microelectronics for Neural, Fuzzy and Bio-inspired Systems (MicroNeuro99), pp. 61-68, Granada, 1999
A. Gacsádi, P. Szolgay, “An analogic CNN algorithm for following continuously moving objects”, Proc. of IEEE Int. Workshop on Cellular Neural Networks and Their Applications, (CNNA 2000), pp. 99-104, Catania, 2000
W.E.L. Grimson, ”From images to surfacesA Computational study of the human early visual system”, The MIT Press, London, England, 1983
P.L. Venetianer, F. Werblin, T. Roska, L. O. Chua, “Analogic CNN algorithms for some image compression and restoration tasks”, IEEE Trans. on Circuits and Systems, Vol.42, pp. 278-284, 1995. [7] Á. Zarándy, T. Roska, F. Werblin, L.O. Chua, “Intelligent image resolution enhancement by the CNN Universal Machine and its relevance to TV picture enhancement”, Proc. of Int. Symposium on Nonlinear Theory and Applications, (NOLTA'95), Vol.2, pp. 701- 706, Las Vegas, 1995
L. Nemes, G. Tóth, T. Roska, A. Radványi, “Analogic CNN algorithms for 3D interpolation-approximation and object rotation using controlled switched templates”, Int. Journal of Circuit Theory and Applications, Vol 24, pp. 409-424, 1996
L.O. Chua, M. Hasler, G.S. Moschytz, J.Neirynck, “Autonomous cellular neural network: A unified paradigm for pattern formation and active wave propagation”, IEEE Trans. on Circuits and Systems, Vol.42, pp. 559-577, 1995
W.E.L. Grimson “An implementation of a computational theory of visual surface interpolation”, Computer Vision, Graphics and Image Processing 22, pp. 39-69, 1983. [11] “CadetWin”, User's Manual, Analogical and Neural Computing Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, 1999.