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  • 标题:back propagation neural network, financial indicators, financial report fraud, data mining
  • 本地全文:下载
  • 作者:D.A. Shepelev ; V.P. Bozhkova ; E.I. Ershov
  • 期刊名称:Computer Optics / Компьютерная оптика
  • 印刷版ISSN:0134-2452
  • 电子版ISSN:2412-6179
  • 出版年度:2020
  • 卷号:44
  • 期号:4
  • 页码:671-679
  • DOI:10.18287/2412-6179-CO-754
  • 语种:English
  • 出版社:Samarskii Natsional'nyi Issledovatel'skii Universitet imeni Akademika S.P. Koroleva,Samara National Research University
  • 摘要:This paper considers methods for simulating color underwater images based on real terrestrial images. Underwater image simulation is widely used for developing and testing methods for improving underwater images. A large group of existing methods uses the same deterministic image transformation model ignoring the presence of noise in images. The paper demonstrates that this significantly affects the overall quality of underwater images simulation. It is shown both theoretically and numerically that the accuracy of the signal-to-noise ratio of underwater images simulated using a deterministic transformation decreases with increasing distance to the object. To solve this problem, a new model of image transformation for simulating underwater images based on terrestrial images is proposed, which considers the presence of noise in the image and is compatible with all simulating methods from the group under consideration. The paper presents the results of the simulation based on the existing and proposed models, showing that at long distances, the new results are better consistent with real data.
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