title: Ð£Ð´Ð°Ð»ÐµÐ½Ð¸Ðµ ÑÑÐ¼Ð° Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ ÑÐ¾ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐ¼Ð¸ ÑÐ»Ð¾ÑÐ¼Ð¸
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: Image denoising with generative adversarial networks with convolutional layers
Creators: name: ÐÐ°Ð»Ð¾Ð², ÐÐ»ÑÑ
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: 2020
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐ¼ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸
machine learning
neural networks
deep learning
image noise
artificial intelligence
convolutional neural networks

description: Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð·Ð°Ð´Ð°ÑÐ° ÑÐ´Ð°Ð»ÐµÐ½Ð¸Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð²Ð¸Ð´Ð¾Ð² ÑÑÐ¼Ð¾Ð² Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. ÐÐ»Ñ Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ ÑÑÐ¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿ÑÐµÐ´Ð¿Ð¾Ð»Ð°Ð³Ð°ÐµÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¸ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¾ÑÐ½Ð¾Ð²Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½ÑÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ´Ð°Ð»ÐµÐ½Ð¸Ñ ÑÑÐ¼Ð°, Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ñ Ð´Ð°Ð½Ð½ÑÐµ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐµÐ¹ÑÐ¾ÑÐµÑÑ Ð±ÑÐ»Ð° Ð¾Ð±ÑÑÐµÐ½Ð° Ð½Ð° Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÑ ÑÐ°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ·ÑÐºÐ° Python, Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº Ð´Ð»Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Tensorflow Ð¸ Keras, ÑÑÐµÐ´Ñ IPython Ñ Ð¾Ð±Ð¾Ð»Ð¾ÑÐºÐ¾Ð¹ Jupyter Notebook. ÐÐ»Ñ Ð¾ÑÑÐ¸ÑÐ¾Ð²ÐºÐ¸ Ð³ÑÐ°ÑÐ¸ÐºÐ¾Ð² Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ° Matplotlib. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð° Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾, ÑÐµÐ¼ ÑÑÐ°Ð½Ð´Ð°ÑÑÐ½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ, ÑÐ´Ð°Ð»ÑÑÑ ÑÑÐ¼ Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐ½Ð½Ð¾Ð³Ð¾ ÑÐ¸Ð¿Ð°.
description: The work considers the task of removing several types of noise from an image. The work discusses the technologies and mathematical foundations of the construction of neural networks, and also analyzes the popular architecture of neural networks. An architecture of a neural network was developed to solve the problem of noise removal, dataset wes prepared for training this network. The neural network was trained on the prepared data. All work was carried out using the Python language, Tensorflow and Keras machine learning libraries, and the IPython environment with the Jupyter Notebook shell. To draw graphs and images, the Matplotlib library was used. The result of the work is a trained neural network, which is able to more effectively than standard methods remove noise from images of a certain type.