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 Key Takeaways
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Objective & Hypothesis:In this paper some new interpolation methods using cellular neural networks (CNN) are proposed.
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Key Findings & Data:The templates have only linear feedback component with a 3*3 dimension.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
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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Cite This Article
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
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