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• W3124354039 abstract "The article discusses the influence of the choice of a color model for representing a raster image on the efficiency of increasing its resolution using an artificial neural network. The article begins with the essence of the research. After that, the process of obtaining for training networks with an identical topology of a dataset using different color models of image representation, namely RGB, HSV, L * a * b *, NTSC and YCbCr, is described. After that, an assessment of the effectiveness of trained neural networks on the previously described datasets is given. To assess the performance of an artificial neural network, two algorithms are used: SSIM and PSNR. As a result of the assessment, the networks using RGB and YCbCr color models showed the highest results. At the end of the article, there are reflections on the reasons for this result." @default.
• W3124354039 created "2021-02-01" @default.
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• W3124354039 date "2021-01-19" @default.
• W3124354039 modified "2023-09-24" @default.
• W3124354039 title "Analysis of the relationship between the choice of the color model for the representation of images and the efficiency of solving the problem of improving the image resolution" @default.
• W3124354039 cites W2941850176 @default.
• W3124354039 doi "https://doi.org/10.1088/1757-899x/1029/1/012099" @default.
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