Noisy image enhancements using deep learning techniques
Daurenbekov K. Aitimova U. Dauitbayeva A. Sankibayev A. Tulegenova E. Yerzhan A. Yerzhanova A. Mukhamedrakhimova G.
February 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 1811 - 818 pp.
This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
Deep learning , Image processing , Machine learning , Multitasking learning model , Noisy image
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Department for Student Affairs, S. Seifullin Kazakh AgroTechnical Research University, Astana, Kazakhstan
Department of Information Systems, S. Seifullin Kazakh AgroTechnical Research University, Astana, Kazakhstan
Department of Computer Science, Korkyt Ata Kyzylorda University, Kyzylorda, Kazakhstan
Department of Artificial Intelligence Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Telecommunications and Innovative Technologies, Almaty University of Power Engineering and Telecommunications named after G. Daukeev, Almaty, Kazakhstan
Department of Technological Machines and Equipment, Faculty of Technology, S. Seifullin Kazakh Agrotechnical Research University, Astana, Kazakhstan
Department of Radio Engineering, Electronics and Telecommunications, Faculty of Physics and Technology, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department for Student Affairs
Department of Information Systems
Department of Computer Science
Department of Artificial Intelligence Technologies
Department of Telecommunications and Innovative Technologies
Department of Technological Machines and Equipment
Department of Radio Engineering
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