title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ð¸ Ð¼ÑÐ»ÑÑÐ¸Ð¿Ð»Ð¸ÐºÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÑÐ¼Ð° Ð½Ð° ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of convolutional neural network for filtering multiplicate noise on radar images
Creators: name: ÐÐ¾Ð¿ÑÑÐ¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ
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ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ ÑÑÐ¼Ð°
Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ ÑÐ¸Ð»ÑÑÑÐ¾Ð²
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð¿ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
radar image
noise
noise filtering
filters parameters
neural networks
optical image
deep learning

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ð¸ ÑÑÐ¼Ð° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÐ»Ñ ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ð¸ ÑÑÐ¼Ð° Ð½Ð° ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ ÑÐ¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÑÐµÐ½ÐºÐ° ÑÐ°Ð±Ð¾ÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÑÐ¿ÐµÑÐ¸Ð°Ð»ÑÐ½ÑÑ Ð¼ÐµÑÑÐ¸Ðº ÐºÐ°ÑÐµÑÑÐ²Ð° Ð½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¼ Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ð¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¾ÑÐºÑÑÑÑÐµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸: Python. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ: Python. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ ÑÐ´Ð°Ð»ÑÑÑ ÑÑÐ¼ Ñ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹.
description: The subject of the graduate qualification work is âradar imagesâ. The given work is devoted to developing and apply noise filtering algorithms based on deep learning methods for radar images. Various designs of artificial neural networks are used to filter noise in radar images. The performance of the models was assessed using special quality metrics in the processed data set. A comparison of the neural network course with classical methods was also carried out. Open educational resources and programs for searching and analyzing information were used. Development automation tools used: Python. Software used: Python. The result is a trained neural network model that can effectively remove noise from radar images.