Image noise reduction by deep learning methods
Uzakkyzy N. Ismailova A. Ayazbaev T. Beldeubayeva Z. Kodanova S. Utenova B. Satybaldiyeva A. Kaldarova M.
December 2023Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2023#13Issue 66855 - 6861 pp.
Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
Deep learning , Image processing , Machine learning , Noisy image , Removing raindrops , Traces of snow on the images
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Department of Computer and Software Engineering, Faculty of Information Technology, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information Systems, S. Seifullin Кazakh Agrotechnical University, Astana, Kazakhstan
Department of Information and Communication Technologies, Faculty of Information Technology, International Taraz Innovative Institute, Taraz, Kazakhstan
Faculty of Information Technologies, Atyrau University of Oil and Gas named after Safi Utebaev, Atyrau, Kazakhstan
Higher School of Information Technology and Engineering, Astana International University, Astana, Kazakhstan
Department of Information Systems, S. Seifullin Кazakh Agrotechnical University, Astana, 010000, Kazakhstan
Department of Computer and Software Engineering
Department of Information Systems
Department of Information and Communication Technologies
Faculty of Information Technologies
Higher School of Information Technology and Engineering
Department of Information Systems
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