# Neural Network System for Recognizing Images Affected by Random-Valued Impulse Noise
## 1 Introduction
### Related Works  
Among all artificial intelligence technologies, convolutional neural networks (CNNs) are the most workable technology for pattern recognition, since such a ratio of hardware costs and the resulting accuracy is recognized as the most effective [9]. Among the main areas of application of neural networks are forecasting, decision-making, pattern recognition,optimization, and data analysis. Neural networks underlie most modern speech recognition and synthesis systems, as well as image recognition and processing. In [10], a modular model for predicting the level of tides was proposed, which could predict the level of tides by analyzing not only the movements of celestial bodies but also environmental factors. The work [11] is devoted to improving the accuracy of predicting metro traffic flows using a neural network with three recurrent blocks and a long short-term memory (LSTM) network. Convolutional networks have gained great popularity in the field of medical diagnostics. The authors of [12] used CNN and LSTM for natural language processing to extract features from an mRNA sequence to predict the miRNA sequence. The work [13] presented an improved cancer segmentation and detection method based on a generalized loss function with functional parameters determined adaptively during model training, to provide a universal basis for optimal decision-making based on a neural network. The problem of decision support under conditions of uncertainty can be solved using a fuzzy set model based on a backpropagation neural network [14]. In [15], a self-organizing computing network was presented based on the concepts of fuzzy conditions, beliefs, probabilities, and neural networks. It was presented in order to make decisions in intelligent systems that are necessary to process data sets with various types of data. The task of recognition and classification is one of the main tasks that neural networks solve. Recently developed capsule networks have already been used successfully for text recognition tasks [16]. Also, neural networks have been successfully applied to solve global optimization problems [17]. However, despite the scope of the neural network, the main task that is solved in all the above works is the task of increasing the recognition accuracy of given objects. On the other hand, when creating, transmitting, and processing images, various noises arise [18], such as Gaussian, salt-pepper, Poisson, impulse, speckle noise, etc. Noise distorts image pixels. It is a destructive factor when training neural networks and it complicates the recognition and classification of objects not only for computer vision systems but also for the human eye [19]. Various types of noise, such as impulse noise, missing image samples, image packet loss, corrupted images, and distorted images, affect image quality and render images unsuitable for processing. For this reason, there is a search for a development of methods and for algorithms to solve the problems of cleaning digital images from various kinds of noise. It should be noted that one of the most common types of noise that occurs when receiving, transmitting, and recording images is impulse noise. Impulse noise is explained by the fact that the intensity of the damaged pixel differs from the intensity of its neighbors [20]. In [21], for the task of removing impulse noise, the authors used non-linear filters; for example, an ascending M-estimator was introduced in the modified nearest neighbor filter. Work [22] is devoted to the use of a median filter, which replaces the value of the brightness of a distorted pixel with the average of its neighbors. Adaptive median filtering first determines if a pixel is skewed and then changes its value to avoid blurry objects [23]. The research [24] offers a convolutional neural network that can recognize noisy images without pre-processing by adding a noise map layer and an adaptive resizing layer, and by improving the overall noise resilience of the CNN.