In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.

In the paper, a family of switching filters designed for the impulsive noise removal in color images is analyzed. The framework of the proposed denoising techniques is based on the concept of cumulated distances between the processed pixel and its neighbors. To increase the filtering efficiency, a robust scheme, in which the sum of distances to only the most similar pixels of the neighborhood serves as a measure of impulsiveness, was elaborated. As this trimmed measure is dependent on the image local structure, an adaptive mechanism was also incorporated. Additionally, a very fast design, which enables image denoising in practical applications, is proposed and the choice of the filter output, which is used to replace the noisy pixels, is discussed. The described family of filters was evaluated on a large set of natural test images and compared with the state-of-the-art restoration methods. The analysis of the achieved results shows that the novel filters outperform the existing techniques in terms of both denoising accuracy and computational complexity. In this way, the proposed techniques can be recommended for the application in various image and video enhancement tasks.

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

This article presents a numerical model of electromagnetic levitation melting and its experimental validation. Levitation melting uses the phenomenon of magnetic induction to float a melted, usually metallic, conductor in an electromagnetic field. With the appropriate configuration of the coil (the source of the alternating magnetic field), the eddy currents induced in the molten batch interact with the coil magnetic field, which causes the melted metal to float without direct contact with any element of the heating system. Such a contactless process is very beneficial for melting very reactive metals (e.g., titanium) or metals with a high melting point (e.g., tungsten). The main disadvantage of levitation melting is the low efficiency of the process. The goal of the authors is to develop, by means of a numerical simulation and optimization tools, a system for levitation melting with acceptable efficiency. To achieve this, it is necessary to develop a reliable and representative computational model. The proposed model includes an analysis of the electromagnetic field, with innovative modeling of the convective heat transport. Experimental validation of the model was performed using aluminum alloy, due to the lack of the need to use a protective atmosphere and the ease of measurements. The measurements included electrical values, the melted batch positions during levitation, the melting time, and the temperature distribution in its area. The verification showed that the compliance between the computational model and the simulation for the position of the batch was accurate to 2 mm ($6.25$%), and the consistency of the batch melting time was accurate to 5 s ($5.49$%). The studies confirmed the good representativeness of the developed numerical model, which makes it a useful tool for the future optimization of the levitation melting system.

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

Deep learning has been widely applied in many computer vision tasks due to its impressive capability of automatic feature extraction and classification. Recently, deep neural networks have been used in image denosing, but most of the proposed approaches were designed for Gaussian noise suppression. Therefore, in this paper, we address the problem of impulsive noise reduction in color images using Denoising Convolutional Neural Networks (DnCNN). This network architecture utilizes the concept of deep residual learning and is trained to learn the residual image instead of the directly denoised one. Our preliminary results show that direct application of DnCNN allows to achieve significantly better results than the state-of-the-art filters designed for impulsive noise in color images.