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APPLYING MACHINE LEARNING TO IMPROVE A TEXTURE TYPE IMAGE

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dc.contributor.author Tussupov, Jamalbek
dc.contributor.author Kozhabai, Kairat
dc.contributor.author Bayegizova, Aigulim
dc.contributor.author Kassenova, Leila
dc.contributor.author Manbetova, Zhanat
dc.contributor.author Glazyrina, Natalya
dc.contributor.author Bersugir, Mukhamedi
dc.contributor.author Yeginbayev, Miras
dc.date.accessioned 2024-10-09T07:40:47Z
dc.date.available 2024-10-09T07:40:47Z
dc.date.issued 2023
dc.identifier.issn 17293774
dc.identifier.other DOI 10.15587/1729-4061.2023.275984
dc.identifier.uri http://rep.enu.kz/handle/enu/17560
dc.description.abstract The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are considered. The first step in image processing is image enhancement. The issues of signal image processing remain in the focus of attention of various specialists. Currently, along with the development of information technology, the automatic improvement of images used in any field of science is one of the urgent problems. Images were analyzed as objects: state license plates of cars, faces, sections of the field on satellite images. In this work, we propose to use the models of Super-Resolution Generative Adversarial Network (SRGAN), Extended Super-Resolution Generative Adversarial Networks (ERSGAN). For this, an experiment was conducted, the purpose of which was to retrain the trained ESRGAN model with three different architectures of the convolutional neural network, i. e. VGG19, MobileNet2V, ResNet152V2 to add perceptual loss (by pixels), also add more sharpness to the prediction of the test image, and compare the performance of each retrained model. As a result of the study, the use of convolutional neural networks to improve the image showed high accuracy, that is, on average ESRGAN+MobileNETV2 – 91 %, ESRGAN+VGG19 – 86 %, ESRGAN+ +ResNet152V2 – 96 % ru
dc.language.iso en ru
dc.publisher Eastern-European Journal of Enterprise Technologies ru
dc.relation.ispartofseries Том 2, Выпуск 2-122, Страницы 13 - 18;
dc.subject ERSGAN ru
dc.subject image processing ru
dc.subject machine learning ru
dc.subject MobileNet2V ru
dc.subject ResNet152V2 ru
dc.subject SRGAN ru
dc.subject Super-Resolution ru
dc.subject VGG19 ru
dc.title APPLYING MACHINE LEARNING TO IMPROVE A TEXTURE TYPE IMAGE ru
dc.type Article ru


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